{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Slopes\n",
    "\n",
    "Bambi's sub-package `interpret` features a set of functions to help interpret complex regression models. The sub-package is inspired by the R package [marginaleffects](https://marginaleffects.com/chapters/predictions.html#conditional-predictions). In this notebook we will discuss two functions `slopes` and `plot_slopes`. These two functions allow the modeler to easier interpret slopes, either by a inspecting a summary output or plotting them.\n",
    "\n",
    "Below, it is described why estimating the slope of the prediction function is useful in interpreting generalized linear models (GLMs), how this methodology is implemented in Bambi, and how to use `slopes` and `plot_slopes`. It is assumed that the reader is familiar with the basics of GLMs. If not, refer to the Bambi [Basic Building Blocks](https://bambinos.github.io/bambi/notebooks/how_bambi_works.html#Link-functions) example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interpretation of Regression Coefficients\n",
    "\n",
    "Assuming we have fit a linear regression model of the form\n",
    "\n",
    "$$y = \\beta_0 + \\beta_1 x_1 + \\beta_2 x_2 + \\dots + \\beta_k x_k + \\epsilon$$\n",
    "\n",
    "the \"safest\" interpretation of the regression coefficients $\\beta$ is as a comparison between two groups of items that differ by $1$ in the relevant predictor variable $x_i$ while being identical in all the other predictors. Formally, the predicted difference between two items $i$ and $j$ that differ by an amount $n$ on predictor $k$, but are identical on all other predictors, the predicted difference is $y_i - y_j$ is $\\beta_kx$, on average.\n",
    "\n",
    "However, once we move away from a regression model with a Gaussian response, the identity function, and no interaction terms, the interpretation of the coefficients are not as straightforward. For example, in a logistic regression model, the coefficients are on a different scale and are measured in logits (log odds), not probabilities or percentage points. Thus, you cannot interpret the coefficents as a \"one unit increase in $x_k$ is associated with an $n$ percentage point decrease in $y$\". First, the logits must be converted to the probability scale. Secondly, a one unit change in $x_k$ may produce a larger or smaller change in the outcome, depending upon how far away from zero the logits are. \n",
    "\n",
    "`slopes` and `plot_slopes`, by default, computes quantities of interest on the response scale for GLMs. For example, for a logistic regression model, this is the probability scale, and for a Poisson regression model, this is the count scale."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting interaction effects\n",
    "\n",
    "Specifying interactions in a regression model is a way of allowing parameters to be conditional on certain aspects of the data. By contrast, for a model with no interactions, the parameters are **not** conditional and thus, the value of one parameter is not dependent on the value of another covariate. However, once interactions exist, multiple parameters are always in play at the same time. Additionally, interactions can be specified for either categorical, continuous, or both types of covariates. Thus, making the interpretation of the parameters more difficult.\n",
    "\n",
    "With GLMs, every covariate essentially interacts with itself because of the link function. To demonstrate parameters interacting with themselves, consider the mean of a Gaussian linear model with an identity link function\n",
    "\n",
    "$$\\mu = \\alpha + \\beta x$$\n",
    "\n",
    "where the rate of change in $\\mu$ with respect to $x$ is just $\\beta$, i.e., the rate of change is constant no matter what the value of $x$ is. But when we consider GLMs with link functions used to map outputs to exponential family distribution parameters, calculating the derivative of the mean output $\\mu$ with respect to the predictor is not as straightforward as in the Gaussian linear model. For example, computing the rate of change in a binomial probability $p$ with respect to $x$\n",
    "\n",
    "$$p = \\frac{exp(\\alpha + \\beta x)}{1 + exp(\\alpha + \\beta x)}$$\n",
    "\n",
    "And taking the derivative of $p$ with respect to $x$ yields\n",
    "\n",
    "$$\\frac{\\partial p}{\\partial x} = \\frac{\\beta}{2(1 + cosh(\\alpha + \\beta x))}$$\n",
    "\n",
    "Since $x$ appears in the derivative, the impact of a change in $x$ depends upon $x$, i.e., an interaction with itself even though no interaction term was specified in the model.Thus, visualizing the rate of change in the mean response with respect to a covariate $x$ becomes a useful tool in interpreting GLMs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Average Predictive Slopes\n",
    "\n",
    "Here, we adopt the notation from Chapter 14.4 of [Regression and Other Stories](https://avehtari.github.io/ROS-Examples/) to first describe average predictive differences which is essential to computing `slopes`, and then secondly, average predictive slopes. Assume we have fit a Bambi model predicting an outcome $Y$ based on inputs $X$ and parameters $\\theta$. Consider the following scalar inputs:\n",
    "\n",
    "$$w: \\text{the input of interest}$$\n",
    "$$c: \\text{all the other inputs}$$\n",
    "$$X = (w, c)$$\n",
    "\n",
    "In contrast to `comparisons`, for `slopes` we are interested in comparing $w^{\\text{value}}$ to $w^{\\text{value}+\\epsilon}$ (perhaps age = 60 and 60.0001 respectively) with all other inputs $c$ held constant. The _predictive difference_ in the outcome changing **only** $w$ is:\n",
    "\n",
    "$$\\text{average predictive difference} = \\mathbb{E}(y|w^{\\text{value}+\\epsilon}, c, \\theta) - \\mathbb{E}(y|w^{\\text{value}}, c, \\theta)$$\n",
    "\n",
    "Selecting $w$ and $w^{\\text{value}+\\epsilon}$ and averaging over all other inputs $c$ in the data gives you a new \"hypothetical\" dataset and corresponds to counting all pairs of transitions of $(w^\\text{value})$ to $(w^{\\text{value}+\\epsilon})$, i.e., differences in $w$ with $c$ held constant. The difference between these two terms is the average predictive difference.\n",
    "\n",
    "However, to obtain the slope estimate, we need to take the above formula and divide by $\\epsilon$ to obtain the _average predictive slope_:\n",
    "\n",
    "$$\\text{average predictive slope} = \\frac{\\mathbb{E}(y|w^{\\text{value}+\\epsilon}, c, \\theta) - \\mathbb{E}(y|w^{\\text{value}}, c, \\theta)}{\\epsilon}$$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing Slopes\n",
    "\n",
    "The objective of `slopes` and `plot_slopes` is to compute the rate of change (slope) in the mean of the response $y$ with respect to a small change $\\epsilon$ in the predictor $x$ conditional on other covariates $c$ specified in the model. $w$ is specified by the user and the original value is either provided by the user, else a default value (the mean) is computed by Bambi. The values for the other covariates $c$ specified in the model can be determined under the following three scenarios:\n",
    "\n",
    "1. user provided values \n",
    "2. a grid of equally spaced and central values\n",
    "3. empirical distribution (original data used to fit the model)\n",
    "\n",
    "In the case of (1) and (2) above, Bambi assembles all pairwise combinations (transitions) of $w$ and $c$ into a new \"hypothetical\" dataset. In (3), Bambi uses the original $c$, and adds a small amount $\\epsilon$ to each unit of observation's $w$. In each scenario, predictions are made on the data using the fitted model. Once the predictions are made, comparisons are computed using the posterior samples by taking the difference in the predicted outcome for each pair of transitions and dividing by $\\epsilon$. The average of these slopes is the average predictive slopes.\n",
    "\n",
    "For variables $w$ with a string or categorical data type, the `comparisons` function is called to compute the expected difference in group means. Please refer to the [comparisons](https://bambinos.github.io/bambi/notebooks/plot_comparisons.html) documentation for more details.\n",
    "\n",
    "Below, we present several examples showing how to use Bambi to perform these computations for us, and to return either a summary dataframe, or a visualization of the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import arviz as az\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import warnings\n",
    "\n",
    "import bambi as bmb\n",
    "\n",
    "warnings.simplefilter(action=\"ignore\", category=FutureWarning)\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression\n",
    "\n",
    "To demonstrate `slopes` and `plot_slopes`, we will use the [well switching dataset](https://vincentarelbundock.github.io/Rdatasets/doc/carData/Wells.html) to model the probability a household in Bangladesh switches water wells. The data are for an area of Arahazar Upazila, Bangladesh. The researchers labelled each well with its level of arsenic and an indication of whether the well was “safe” or “unsafe”. Those using unsafe wells were encouraged to switch. After several years, it was determined whether each household using an unsafe well had changed its well. The data contains $3020$ observations on the following five variables:\n",
    "\n",
    "- `switch`: a factor with levels `no` and `yes` indicating whether the household switched to a new well\n",
    "- `arsenic`: the level of arsenic in the old well (measured in micrograms per liter)\n",
    "- `dist`: the distance to the nearest safe well (measured in meters)\n",
    "- `assoc`: a factor with levels `no` and `yes` indicating whether the household is a member of an arsenic education group\n",
    "- `educ`: years of education of the household head\n",
    "\n",
    "First, a logistic regression model with no interactions is fit to the data. Subsequently, to demonstrate the benefits of `plot_slopes` in interpreting interactions, we will fit a logistic regression model with an interaction term."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rownames</th>\n",
       "      <th>switch</th>\n",
       "      <th>arsenic</th>\n",
       "      <th>distance</th>\n",
       "      <th>education</th>\n",
       "      <th>association</th>\n",
       "      <th>dist100</th>\n",
       "      <th>educ4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>2.36</td>\n",
       "      <td>16.826</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>0.16826</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>0.71</td>\n",
       "      <td>47.322</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>0.47322</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>2.07</td>\n",
       "      <td>20.967</td>\n",
       "      <td>10</td>\n",
       "      <td>no</td>\n",
       "      <td>0.20967</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.15</td>\n",
       "      <td>21.486</td>\n",
       "      <td>12</td>\n",
       "      <td>no</td>\n",
       "      <td>0.21486</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.10</td>\n",
       "      <td>40.874</td>\n",
       "      <td>14</td>\n",
       "      <td>yes</td>\n",
       "      <td>0.40874</td>\n",
       "      <td>3.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rownames switch  arsenic  distance  education association  dist100  educ4\n",
       "0         1    yes     2.36    16.826          0          no  0.16826    0.0\n",
       "1         2    yes     0.71    47.322          0          no  0.47322    0.0\n",
       "2         3     no     2.07    20.967         10          no  0.20967    2.5\n",
       "3         4    yes     1.15    21.486         12          no  0.21486    3.0\n",
       "4         5    yes     1.10    40.874         14         yes  0.40874    3.5"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/carData/Wells.csv\")\n",
    "data[\"switch\"] = pd.Categorical(data[\"switch\"])\n",
    "data[\"dist100\"] = data[\"distance\"] / 100\n",
    "data[\"educ4\"] = data[\"education\"] / 4\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Modeling the probability that switch==no\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [Intercept, dist100, arsenic, educ4]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e6731c93d6314862b2760592ed6589de",
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       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 6 seconds.\n"
     ]
    }
   ],
   "source": [
    "well_model = bmb.Model(\n",
    "    \"switch ~ dist100 + arsenic + educ4\",\n",
    "    data,\n",
    "    family=\"bernoulli\"\n",
    ")\n",
    "\n",
    "well_idata = well_model.fit(\n",
    "    draws=1000, \n",
    "    target_accept=0.95, \n",
    "    random_seed=1234, \n",
    "    chains=4\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User provided values\n",
    "\n",
    "First, an example of scenario 1 (user provided values) is given below. In both `plot_slopes` and `slopes`, $w$ and $c$ are represented by `wrt` (with respect to) and `conditional`, respectively. The modeler has the ability to pass their own values for `wrt` and `conditional` by using a dictionary where the key-value pairs are the covariate and value(s) of interest.\n",
    "\n",
    "For example, if we wanted to compute the slope of the probability of switching wells for a typical `arsenic` value of $1.3$ conditional on a range of `dist` and `educ` values, we would pass the following dictionary in the code block below. By default, for $w$, Bambi compares $w^\\text{value}$ to $w^{\\text{value} + \\epsilon}$ where $\\epsilon =$ `1e-4`. However, the value for $\\epsilon$ can be changed by passing a value to the argument `eps`. \n",
    "\n",
    "Thus, in this example, $w^\\text{value} = 1.3$ and $w^{\\text{value} + \\epsilon} = 1.3001$. The user is not limited to passing a list for the values. A `np.array` can also be used. Furthermore, Bambi by default, maps the order of the dict keys to the main, group, and panel of the matplotlib figure. Below, since `dist100` is the first key, this is used for the x-axis, and `educ4` is used for the group (color). If a third key was passed, it would be used for the panel (facet)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = bmb.interpret.plot_slopes(\n",
    "    well_model,\n",
    "    well_idata,\n",
    "    wrt={\"arsenic\": 1.3},\n",
    "    conditional={\"dist100\": [0.20, 0.50, 0.80], \"educ4\": [1.00, 1.20, 2.00]},\n",
    ")\n",
    "fig.set_size_inches(7, 3)\n",
    "fig.axes[0].set_ylabel(\"Slope of Well Switching Probability\");"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The plot above shows that, for example, conditional on `dist100` $= 0.2$ and `educ4` $= 1.0$ a unit increase in `arsenic` is associated with households being $11$% less likely to switch wells. Notice that even though we fit a logistic regression model where the coefficients are on the log-odds scale, the `slopes` function returns the slope on the probability scale. Thus, we can interpret the y-axis (slope) as the expected change in the probability of switching wells for a unit increase in `arsenic` conditional on the specified covariates.\n",
    "\n",
    "`slopes` can be called directly to view a summary dataframe that includes the term name, estimate type (discussed in detail in the _interpreting coefficients as an elasticity_ section), values $w$ used to compute the estimate, the specified conditional covariates $c$, and the expected slope of the outcome with the uncertainty interval (by default the $94$% highest density interval is computed)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>estimate_type</th>\n",
       "      <th>value</th>\n",
       "      <th>dist100</th>\n",
       "      <th>educ4</th>\n",
       "      <th>estimate</th>\n",
       "      <th>lower_3.0%</th>\n",
       "      <th>upper_97.0%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(1.5, 1.5001)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.110501</td>\n",
       "      <td>-0.128753</td>\n",
       "      <td>-0.092899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(1.5, 1.5001)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>-0.109584</td>\n",
       "      <td>-0.127565</td>\n",
       "      <td>-0.092048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(1.5, 1.5001)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.105399</td>\n",
       "      <td>-0.123199</td>\n",
       "      <td>-0.088894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(1.5, 1.5001)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.115731</td>\n",
       "      <td>-0.135337</td>\n",
       "      <td>-0.097521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(1.5, 1.5001)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.2</td>\n",
       "      <td>-0.115280</td>\n",
       "      <td>-0.134801</td>\n",
       "      <td>-0.097199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(1.5, 1.5001)</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.112818</td>\n",
       "      <td>-0.131913</td>\n",
       "      <td>-0.095104</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(1.5, 1.5001)</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.116923</td>\n",
       "      <td>-0.136423</td>\n",
       "      <td>-0.098302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(1.5, 1.5001)</td>\n",
       "      <td>0.8</td>\n",
       "      <td>1.2</td>\n",
       "      <td>-0.117001</td>\n",
       "      <td>-0.136475</td>\n",
       "      <td>-0.098365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(1.5, 1.5001)</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-0.116597</td>\n",
       "      <td>-0.135591</td>\n",
       "      <td>-0.097564</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      term estimate_type          value  dist100  educ4  estimate  lower_3.0%  \\\n",
       "0  arsenic          dydx  (1.5, 1.5001)      0.2    1.0 -0.110501   -0.128753   \n",
       "1  arsenic          dydx  (1.5, 1.5001)      0.2    1.2 -0.109584   -0.127565   \n",
       "2  arsenic          dydx  (1.5, 1.5001)      0.2    2.0 -0.105399   -0.123199   \n",
       "3  arsenic          dydx  (1.5, 1.5001)      0.5    1.0 -0.115731   -0.135337   \n",
       "4  arsenic          dydx  (1.5, 1.5001)      0.5    1.2 -0.115280   -0.134801   \n",
       "5  arsenic          dydx  (1.5, 1.5001)      0.5    2.0 -0.112818   -0.131913   \n",
       "6  arsenic          dydx  (1.5, 1.5001)      0.8    1.0 -0.116923   -0.136423   \n",
       "7  arsenic          dydx  (1.5, 1.5001)      0.8    1.2 -0.117001   -0.136475   \n",
       "8  arsenic          dydx  (1.5, 1.5001)      0.8    2.0 -0.116597   -0.135591   \n",
       "\n",
       "   upper_97.0%  \n",
       "0    -0.092899  \n",
       "1    -0.092048  \n",
       "2    -0.088894  \n",
       "3    -0.097521  \n",
       "4    -0.097199  \n",
       "5    -0.095104  \n",
       "6    -0.098302  \n",
       "7    -0.098365  \n",
       "8    -0.097564  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmb.interpret.slopes(\n",
    "    well_model,\n",
    "    well_idata,\n",
    "    wrt={\"arsenic\": 1.5},\n",
    "    conditional={\n",
    "        \"dist100\": [0.20, 0.50, 0.80],\n",
    "        \"educ4\": [1.00, 1.20, 2.00]\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since all covariates used to fit the model were also specified to compute the slopes, no default value is used for unspecified covariates. A default value is computed for the unspecified covariates because in order to peform predictions, Bambi is expecting a value for each covariate used to fit the model. Additionally, with GLM models, average predictive slopes are conditional in the sense that the estimate depends on the values of **all** the covariates in the model. Thus, for unspecified covariates, `slopes` and `plot_slopes` computes a default value (mean or mode based on the data type of the covariate). Each row in the summary dataframe is read as \"the slope (or rate of change) of the probability of switching wells with respect to a small change in $w$ conditional on $c$ is $y$\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple slope values\n",
    "\n",
    "Users can also compute slopes on multiple values for `wrt`. For example, if we want to compute the slope of $y$ with respect to `arsenic` $= 1.5$, $2.0$, and $2.5$, simply pass a list or numpy array as the dictionary values for `wrt`. Keeping the conditional covariate and values the same, the following slope estimates are computed below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>estimate_type</th>\n",
       "      <th>value</th>\n",
       "      <th>dist100</th>\n",
       "      <th>educ4</th>\n",
       "      <th>estimate</th>\n",
       "      <th>lower_3.0%</th>\n",
       "      <th>upper_97.0%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(1.5, 1.5001)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.110501</td>\n",
       "      <td>-0.128753</td>\n",
       "      <td>-0.092899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(2.0, 2.0001)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.109584</td>\n",
       "      <td>-0.127565</td>\n",
       "      <td>-0.092048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(2.5, 2.5001)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.105399</td>\n",
       "      <td>-0.123199</td>\n",
       "      <td>-0.088894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(1.5, 1.5001)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>-0.115731</td>\n",
       "      <td>-0.135337</td>\n",
       "      <td>-0.097521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(2.0, 2.0001)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>-0.115280</td>\n",
       "      <td>-0.134801</td>\n",
       "      <td>-0.097199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(2.5, 2.5001)</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.2</td>\n",
       "      <td>-0.112818</td>\n",
       "      <td>-0.131913</td>\n",
       "      <td>-0.095104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      term estimate_type          value  dist100  educ4  estimate  lower_3.0%  \\\n",
       "0  arsenic          dydx  (1.5, 1.5001)      0.2    1.0 -0.110501   -0.128753   \n",
       "1  arsenic          dydx  (2.0, 2.0001)      0.2    1.0 -0.109584   -0.127565   \n",
       "2  arsenic          dydx  (2.5, 2.5001)      0.2    1.0 -0.105399   -0.123199   \n",
       "3  arsenic          dydx  (1.5, 1.5001)      0.2    1.2 -0.115731   -0.135337   \n",
       "4  arsenic          dydx  (2.0, 2.0001)      0.2    1.2 -0.115280   -0.134801   \n",
       "5  arsenic          dydx  (2.5, 2.5001)      0.2    1.2 -0.112818   -0.131913   \n",
       "\n",
       "   upper_97.0%  \n",
       "0    -0.092899  \n",
       "1    -0.092048  \n",
       "2    -0.088894  \n",
       "3    -0.097521  \n",
       "4    -0.097199  \n",
       "5    -0.095104  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiple_values = bmb.interpret.slopes(\n",
    "    well_model,\n",
    "    well_idata,\n",
    "    wrt={\"arsenic\": [1.5, 2.0, 2.5]},\n",
    "    conditional={\n",
    "        \"dist100\": [0.20, 0.50, 0.80], \n",
    "        \"educ4\": [1.00, 1.20, 2.00]\n",
    "        }\n",
    ")\n",
    "\n",
    "multiple_values.head(6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output above is essentially the same as the summary dataframe when we only passed one value to `wrt`. However, now each element (value) in the list gets a small amount $\\epsilon$ added to it, and the slope is calculated for each of these values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditional slopes\n",
    "\n",
    "As stated in the _interpreting interaction effects_ section, interpreting coefficients of multiple interaction terms can be difficult and cumbersome. Thus, `plot_slopes` provides an effective way to visualize the conditional slopes of the interaction effects. Below, we will use the same well switching dataset, but with interaction terms. Specifically, one interaction is added between `dist100` and `educ4`, and another between `arsenic` and `educ4`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Modeling the probability that switch==no\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [Intercept, dist100, arsenic, educ4, dist100:educ4, arsenic:educ4]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1c5784ebf2ca4c1484331abf257ac4a0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 500 tune and 500 draw iterations (2_000 + 2_000 draws total) took 6 seconds.\n"
     ]
    }
   ],
   "source": [
    "well_model_interact = bmb.Model(\n",
    "    \"switch ~ dist100 + arsenic + educ4 + dist100:educ4 + arsenic:educ4\",\n",
    "    data=data,\n",
    "    family=\"bernoulli\"\n",
    ")\n",
    "\n",
    "well_idata_interact = well_model_interact.fit(\n",
    "    draws=500, \n",
    "    tune=500,\n",
    "    target_accept=0.95, \n",
    "    random_seed=1234, \n",
    "    chains=4\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>sd</th>\n",
       "      <th>hdi_3%</th>\n",
       "      <th>hdi_97%</th>\n",
       "      <th>mcse_mean</th>\n",
       "      <th>mcse_sd</th>\n",
       "      <th>ess_bulk</th>\n",
       "      <th>ess_tail</th>\n",
       "      <th>r_hat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Intercept</th>\n",
       "      <td>-0.089</td>\n",
       "      <td>0.121</td>\n",
       "      <td>-0.304</td>\n",
       "      <td>0.151</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1260.0</td>\n",
       "      <td>1151.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dist100</th>\n",
       "      <td>1.317</td>\n",
       "      <td>0.174</td>\n",
       "      <td>0.977</td>\n",
       "      <td>1.623</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.004</td>\n",
       "      <td>1103.0</td>\n",
       "      <td>1217.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arsenic</th>\n",
       "      <td>-0.402</td>\n",
       "      <td>0.061</td>\n",
       "      <td>-0.517</td>\n",
       "      <td>-0.295</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.001</td>\n",
       "      <td>919.0</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>educ4</th>\n",
       "      <td>0.095</td>\n",
       "      <td>0.078</td>\n",
       "      <td>-0.050</td>\n",
       "      <td>0.235</td>\n",
       "      <td>0.002</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1075.0</td>\n",
       "      <td>1137.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dist100:educ4</th>\n",
       "      <td>-0.328</td>\n",
       "      <td>0.107</td>\n",
       "      <td>-0.528</td>\n",
       "      <td>-0.124</td>\n",
       "      <td>0.003</td>\n",
       "      <td>0.002</td>\n",
       "      <td>1102.0</td>\n",
       "      <td>1074.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arsenic:educ4</th>\n",
       "      <td>-0.076</td>\n",
       "      <td>0.043</td>\n",
       "      <td>-0.156</td>\n",
       "      <td>0.004</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>932.0</td>\n",
       "      <td>897.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                mean     sd  hdi_3%  hdi_97%  mcse_mean  mcse_sd  ess_bulk  \\\n",
       "Intercept     -0.089  0.121  -0.304    0.151      0.003    0.002    1260.0   \n",
       "dist100        1.317  0.174   0.977    1.623      0.005    0.004    1103.0   \n",
       "arsenic       -0.402  0.061  -0.517   -0.295      0.002    0.001     919.0   \n",
       "educ4          0.095  0.078  -0.050    0.235      0.002    0.002    1075.0   \n",
       "dist100:educ4 -0.328  0.107  -0.528   -0.124      0.003    0.002    1102.0   \n",
       "arsenic:educ4 -0.076  0.043  -0.156    0.004      0.001    0.001     932.0   \n",
       "\n",
       "               ess_tail  r_hat  \n",
       "Intercept        1151.0    1.0  \n",
       "dist100          1217.0    1.0  \n",
       "arsenic          1021.0    1.0  \n",
       "educ4            1137.0    1.0  \n",
       "dist100:educ4    1074.0    1.0  \n",
       "arsenic:educ4     897.0    1.0  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# summary of coefficients\n",
    "az.summary(well_idata_interact)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coefficients of the linear model are shown in the table above. The interaction coefficents indicate the slope varies in a continuous fashion with the continuous variable.\n",
    "\n",
    "A negative value for `arsenic:dist100` indicates that the \"effect\" of arsenic on the outcome is less negative as distance from the well increases. Similarly, a negative value for `arsenic:educ4` indicates that the \"effect\" of arsenic on the outcome is more negative as education increases. Remember, these coefficients are still on the logit scale. Furthermore, as more variables and interaction terms are added to the model, interpreting these coefficients becomes more difficult. \n",
    "\n",
    "Thus, lets use `plot_slopes` to visually see how the slope changes with respect to `arsenic` conditional on `dist100` and `educ4` changing. Notice in the code block below how parameters are passed to the `subplot_kwargs` and `fig_kwargs` arguments. At times, it can be useful to pass specific `group` and `panel` arguments to aid in the interpretation of the plot. Therefore, `subplot_kwargs` allows the user to manipulate the plotting by passing a dictionary where the keys are `{\"main\": ..., \"group\": ..., \"panel\": ...}` and the values are the names of the covariates to be plotted. `fig_kwargs` are figure level key word arguments such as `figsize` and `sharey`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Default computed for wrt variable: arsenic\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = bmb.interpret.plot_slopes(\n",
    "    well_model_interact,\n",
    "    well_idata_interact,\n",
    "    wrt=\"arsenic\",\n",
    "    conditional={\n",
    "        \"dist100\": np.linspace(0, 4, 50),\n",
    "        \"educ4\": np.arange(0, 5, 1)\n",
    "    },\n",
    "    subplot_kwargs={\"main\": \"dist100\", \"group\": \"educ4\", \"panel\": \"educ4\"},\n",
    "    fig_kwargs={\"figsize\": (16, 6), \"sharey\": True, \"tight_layout\": True},\n",
    "    legend=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we talk about the plot, you will notice that some messages have been logged to the console. By default `interpret` is _verbose_ and logs a message to the console if a default value is computed for covariates in `conditional` and `wrt`. This is useful because unless the documentation is read, it can be difficult to tell which covariates are having default values computed for. Thus, Bambi has a config file `bmb.config[\"INTERPRET_VERBOSE\"]` where we can specify whether or not to log messages. By default, this is set to true. To turn off logging, set `bmb.config[\"INTERPRET_VERBOSE\"] = False`. From here on, we will turn off logging.\n",
    "\n",
    "With interaction terms now defined, it can be seen how the slope of the outcome with respect to `arsenic` differ depending on the value of `educ4`. Especially in the case of `educ4` $= 4.25$, the slope is more \"constant\", but with greater uncertainty. Lets compare this with the model that does not include any interaction terms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "bmb.config[\"INTERPRET_VERBOSE\"] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = bmb.interpret.plot_slopes(\n",
    "    well_model,\n",
    "    well_idata,\n",
    "    wrt=\"arsenic\",\n",
    "    conditional={\n",
    "        \"dist100\": np.linspace(0, 4, 50),\n",
    "        \"educ4\": np.arange(0, 5, 1)\n",
    "    },\n",
    "    subplot_kwargs={\"main\": \"dist100\", \"group\": \"educ4\", \"panel\": \"educ4\"},\n",
    "    fig_kwargs={\"figsize\": (16, 6), \"sharey\": True, \"tight_layout\": True},\n",
    "    legend=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the non-interaction model, conditional on a range of values for `educ4` and `dist100`, the slopes of the outcome are nearly identical."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unit level slopes\n",
    "\n",
    "Evaluating average predictive slopes at central values for the conditional covariates $c$ can be problematic when the inputs have a large variance since no single central value (mean, median, etc.) is representative of the covariate. This is especially true when $c$ exhibits bi or multimodality. Thus, it may be desireable to use the empirical distribution of $c$ to compute the predictive slopes, and then average over a specific or set of covariates to obtain average slopes. To achieve unit level slopes, do not pass a parameter into `conditional` and or specify `None`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>estimate_type</th>\n",
       "      <th>value</th>\n",
       "      <th>dist100</th>\n",
       "      <th>educ4</th>\n",
       "      <th>estimate</th>\n",
       "      <th>lower_3.0%</th>\n",
       "      <th>upper_97.0%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(2.36, 2.3601)</td>\n",
       "      <td>0.16826</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.084990</td>\n",
       "      <td>-0.106553</td>\n",
       "      <td>-0.065591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(0.71, 0.7101)</td>\n",
       "      <td>0.47322</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.098632</td>\n",
       "      <td>-0.124283</td>\n",
       "      <td>-0.071685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(2.07, 2.0701)</td>\n",
       "      <td>0.20967</td>\n",
       "      <td>2.50</td>\n",
       "      <td>-0.117251</td>\n",
       "      <td>-0.139517</td>\n",
       "      <td>-0.093123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(1.15, 1.1501)</td>\n",
       "      <td>0.21486</td>\n",
       "      <td>3.00</td>\n",
       "      <td>-0.149083</td>\n",
       "      <td>-0.191299</td>\n",
       "      <td>-0.107007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(1.1, 1.1001)</td>\n",
       "      <td>0.40874</td>\n",
       "      <td>3.50</td>\n",
       "      <td>-0.159316</td>\n",
       "      <td>-0.210319</td>\n",
       "      <td>-0.104427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(3.9, 3.9001)</td>\n",
       "      <td>0.69518</td>\n",
       "      <td>2.25</td>\n",
       "      <td>-0.073841</td>\n",
       "      <td>-0.080303</td>\n",
       "      <td>-0.067324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(2.97, 2.9701000000000004)</td>\n",
       "      <td>0.80711</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.108545</td>\n",
       "      <td>-0.124233</td>\n",
       "      <td>-0.094288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(3.24, 3.2401000000000004)</td>\n",
       "      <td>0.55146</td>\n",
       "      <td>2.50</td>\n",
       "      <td>-0.087756</td>\n",
       "      <td>-0.097813</td>\n",
       "      <td>-0.077389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(3.28, 3.2801)</td>\n",
       "      <td>0.52647</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.088006</td>\n",
       "      <td>-0.106682</td>\n",
       "      <td>-0.067846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>(2.52, 2.5201000000000002)</td>\n",
       "      <td>0.75072</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.099874</td>\n",
       "      <td>-0.128025</td>\n",
       "      <td>-0.073512</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      term estimate_type                       value  dist100  educ4  \\\n",
       "0  arsenic          dydx              (2.36, 2.3601)  0.16826   0.00   \n",
       "1  arsenic          dydx              (0.71, 0.7101)  0.47322   0.00   \n",
       "2  arsenic          dydx              (2.07, 2.0701)  0.20967   2.50   \n",
       "3  arsenic          dydx              (1.15, 1.1501)  0.21486   3.00   \n",
       "4  arsenic          dydx               (1.1, 1.1001)  0.40874   3.50   \n",
       "5  arsenic          dydx               (3.9, 3.9001)  0.69518   2.25   \n",
       "6  arsenic          dydx  (2.97, 2.9701000000000004)  0.80711   1.00   \n",
       "7  arsenic          dydx  (3.24, 3.2401000000000004)  0.55146   2.50   \n",
       "8  arsenic          dydx              (3.28, 3.2801)  0.52647   0.00   \n",
       "9  arsenic          dydx  (2.52, 2.5201000000000002)  0.75072   0.00   \n",
       "\n",
       "   estimate  lower_3.0%  upper_97.0%  \n",
       "0 -0.084990   -0.106553    -0.065591  \n",
       "1 -0.098632   -0.124283    -0.071685  \n",
       "2 -0.117251   -0.139517    -0.093123  \n",
       "3 -0.149083   -0.191299    -0.107007  \n",
       "4 -0.159316   -0.210319    -0.104427  \n",
       "5 -0.073841   -0.080303    -0.067324  \n",
       "6 -0.108545   -0.124233    -0.094288  \n",
       "7 -0.087756   -0.097813    -0.077389  \n",
       "8 -0.088006   -0.106682    -0.067846  \n",
       "9 -0.099874   -0.128025    -0.073512  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unit_level = bmb.interpret.slopes(\n",
    "    well_model_interact,\n",
    "    well_idata_interact,\n",
    "    wrt=\"arsenic\",\n",
    "    conditional=None\n",
    ")\n",
    "\n",
    "# empirical distribution\n",
    "print(unit_level.shape[0] == well_model_interact.data.shape[0])\n",
    "unit_level.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rownames</th>\n",
       "      <th>switch</th>\n",
       "      <th>arsenic</th>\n",
       "      <th>distance</th>\n",
       "      <th>education</th>\n",
       "      <th>association</th>\n",
       "      <th>dist100</th>\n",
       "      <th>educ4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>2.36</td>\n",
       "      <td>16.826</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>0.16826</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>0.71</td>\n",
       "      <td>47.322</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>0.47322</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>2.07</td>\n",
       "      <td>20.967</td>\n",
       "      <td>10</td>\n",
       "      <td>no</td>\n",
       "      <td>0.20967</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.15</td>\n",
       "      <td>21.486</td>\n",
       "      <td>12</td>\n",
       "      <td>no</td>\n",
       "      <td>0.21486</td>\n",
       "      <td>3.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>yes</td>\n",
       "      <td>1.10</td>\n",
       "      <td>40.874</td>\n",
       "      <td>14</td>\n",
       "      <td>yes</td>\n",
       "      <td>0.40874</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>yes</td>\n",
       "      <td>3.90</td>\n",
       "      <td>69.518</td>\n",
       "      <td>9</td>\n",
       "      <td>yes</td>\n",
       "      <td>0.69518</td>\n",
       "      <td>2.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>yes</td>\n",
       "      <td>2.97</td>\n",
       "      <td>80.711</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>0.80711</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>yes</td>\n",
       "      <td>3.24</td>\n",
       "      <td>55.146</td>\n",
       "      <td>10</td>\n",
       "      <td>no</td>\n",
       "      <td>0.55146</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>yes</td>\n",
       "      <td>3.28</td>\n",
       "      <td>52.647</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>0.52647</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>yes</td>\n",
       "      <td>2.52</td>\n",
       "      <td>75.072</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>0.75072</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rownames switch  arsenic  distance  education association  dist100  educ4\n",
       "0         1    yes     2.36    16.826          0          no  0.16826   0.00\n",
       "1         2    yes     0.71    47.322          0          no  0.47322   0.00\n",
       "2         3     no     2.07    20.967         10          no  0.20967   2.50\n",
       "3         4    yes     1.15    21.486         12          no  0.21486   3.00\n",
       "4         5    yes     1.10    40.874         14         yes  0.40874   3.50\n",
       "5         6    yes     3.90    69.518          9         yes  0.69518   2.25\n",
       "6         7    yes     2.97    80.711          4         yes  0.80711   1.00\n",
       "7         8    yes     3.24    55.146         10          no  0.55146   2.50\n",
       "8         9    yes     3.28    52.647          0         yes  0.52647   0.00\n",
       "9        10    yes     2.52    75.072          0         yes  0.75072   0.00"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "well_model_interact.data.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above, `unit_level` is the slopes summary dataframe and `well_model_interact.data` is the empirical data used to fit the model. Notice how the values for $c$ are identical in both dataframes. However, for $w$, the values are the original $w$ value plus $\\epsilon$. Thus, the `estimate` value represents the instantaneous rate of change for that unit of observation. However, these unit level slopes are difficult to interpret since each row may have a different slope estimate. Therefore, it is useful to average over (marginalize) the estimates to summarize the unit level predictive slopes."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Marginalizing over covariates\n",
    "\n",
    "Since the empirical distrubution is used for computing the average predictive slopes, the same number of rows ($3020$) is returned as the data used to fit the model. To average over a covariate, use the `average_by` argument. If `True` is passed, then `slopes` averages over all covariates. Else, if a single or list of covariates are passed, then `slopes` averages by the covariates passed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>estimate_type</th>\n",
       "      <th>estimate</th>\n",
       "      <th>lower_3.0%</th>\n",
       "      <th>upper_97.0%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>-0.111296</td>\n",
       "      <td>-0.134928</td>\n",
       "      <td>-0.089245</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      term estimate_type  estimate  lower_3.0%  upper_97.0%\n",
       "0  arsenic          dydx -0.111296   -0.134928    -0.089245"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmb.interpret.slopes(\n",
    "    well_model_interact,\n",
    "    well_idata_interact,\n",
    "    wrt=\"arsenic\",\n",
    "    conditional=None,\n",
    "    average_by=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The code block above is equivalent to taking the mean of the `estimate` and uncertainty columns. For example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "estimate      -0.111296\n",
       "lower_3.0%    -0.134928\n",
       "upper_97.0%   -0.089245\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unit_level[[\"estimate\", \"lower_3.0%\", \"upper_97.0%\"]].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Average by subgroups\n",
    "\n",
    "Averaging over all covariates may not be desired, and you would rather average by a group or specific covariate. To perform averaging by subgroups, users can pass a single or list of covariates to `average_by` to average over specific covariates. For example, if we wanted to average by `educ4`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>estimate_type</th>\n",
       "      <th>educ4</th>\n",
       "      <th>estimate</th>\n",
       "      <th>lower_3.0%</th>\n",
       "      <th>upper_97.0%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>0.00</td>\n",
       "      <td>-0.093169</td>\n",
       "      <td>-0.118536</td>\n",
       "      <td>-0.068794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>0.25</td>\n",
       "      <td>-0.102358</td>\n",
       "      <td>-0.127127</td>\n",
       "      <td>-0.080085</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>0.50</td>\n",
       "      <td>-0.102548</td>\n",
       "      <td>-0.123101</td>\n",
       "      <td>-0.083896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>0.75</td>\n",
       "      <td>-0.106255</td>\n",
       "      <td>-0.124590</td>\n",
       "      <td>-0.089088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>1.00</td>\n",
       "      <td>-0.110657</td>\n",
       "      <td>-0.128600</td>\n",
       "      <td>-0.094565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>1.25</td>\n",
       "      <td>-0.112242</td>\n",
       "      <td>-0.129444</td>\n",
       "      <td>-0.096226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>1.50</td>\n",
       "      <td>-0.114621</td>\n",
       "      <td>-0.132190</td>\n",
       "      <td>-0.096798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>1.75</td>\n",
       "      <td>-0.122119</td>\n",
       "      <td>-0.143431</td>\n",
       "      <td>-0.102749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>2.00</td>\n",
       "      <td>-0.124580</td>\n",
       "      <td>-0.148935</td>\n",
       "      <td>-0.103232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>2.25</td>\n",
       "      <td>-0.124640</td>\n",
       "      <td>-0.150726</td>\n",
       "      <td>-0.101126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>2.50</td>\n",
       "      <td>-0.129838</td>\n",
       "      <td>-0.160267</td>\n",
       "      <td>-0.102663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>2.75</td>\n",
       "      <td>-0.136295</td>\n",
       "      <td>-0.171705</td>\n",
       "      <td>-0.105108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>3.00</td>\n",
       "      <td>-0.134872</td>\n",
       "      <td>-0.172066</td>\n",
       "      <td>-0.100304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>3.25</td>\n",
       "      <td>-0.155237</td>\n",
       "      <td>-0.203619</td>\n",
       "      <td>-0.109965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>3.50</td>\n",
       "      <td>-0.141038</td>\n",
       "      <td>-0.185914</td>\n",
       "      <td>-0.098660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>3.75</td>\n",
       "      <td>-0.136778</td>\n",
       "      <td>-0.180398</td>\n",
       "      <td>-0.093146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>4.00</td>\n",
       "      <td>-0.136485</td>\n",
       "      <td>-0.183766</td>\n",
       "      <td>-0.088661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>4.25</td>\n",
       "      <td>-0.173986</td>\n",
       "      <td>-0.238259</td>\n",
       "      <td>-0.101553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       term estimate_type  educ4  estimate  lower_3.0%  upper_97.0%\n",
       "0   arsenic          dydx   0.00 -0.093169   -0.118536    -0.068794\n",
       "1   arsenic          dydx   0.25 -0.102358   -0.127127    -0.080085\n",
       "2   arsenic          dydx   0.50 -0.102548   -0.123101    -0.083896\n",
       "3   arsenic          dydx   0.75 -0.106255   -0.124590    -0.089088\n",
       "4   arsenic          dydx   1.00 -0.110657   -0.128600    -0.094565\n",
       "5   arsenic          dydx   1.25 -0.112242   -0.129444    -0.096226\n",
       "6   arsenic          dydx   1.50 -0.114621   -0.132190    -0.096798\n",
       "7   arsenic          dydx   1.75 -0.122119   -0.143431    -0.102749\n",
       "8   arsenic          dydx   2.00 -0.124580   -0.148935    -0.103232\n",
       "9   arsenic          dydx   2.25 -0.124640   -0.150726    -0.101126\n",
       "10  arsenic          dydx   2.50 -0.129838   -0.160267    -0.102663\n",
       "11  arsenic          dydx   2.75 -0.136295   -0.171705    -0.105108\n",
       "12  arsenic          dydx   3.00 -0.134872   -0.172066    -0.100304\n",
       "13  arsenic          dydx   3.25 -0.155237   -0.203619    -0.109965\n",
       "14  arsenic          dydx   3.50 -0.141038   -0.185914    -0.098660\n",
       "15  arsenic          dydx   3.75 -0.136778   -0.180398    -0.093146\n",
       "16  arsenic          dydx   4.00 -0.136485   -0.183766    -0.088661\n",
       "17  arsenic          dydx   4.25 -0.173986   -0.238259    -0.101553"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# average by educ4\n",
    "bmb.interpret.slopes(\n",
    "    well_model_interact,\n",
    "    well_idata_interact,\n",
    "    wrt=\"arsenic\",\n",
    "    conditional=None,\n",
    "    average_by=\"educ4\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>estimate_type</th>\n",
       "      <th>educ4</th>\n",
       "      <th>dist100</th>\n",
       "      <th>estimate</th>\n",
       "      <th>lower_3.0%</th>\n",
       "      <th>upper_97.0%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00591</td>\n",
       "      <td>-0.086666</td>\n",
       "      <td>-0.111418</td>\n",
       "      <td>-0.065328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02409</td>\n",
       "      <td>-0.097186</td>\n",
       "      <td>-0.126608</td>\n",
       "      <td>-0.070509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02454</td>\n",
       "      <td>-0.056941</td>\n",
       "      <td>-0.065824</td>\n",
       "      <td>-0.048077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02791</td>\n",
       "      <td>-0.098558</td>\n",
       "      <td>-0.128713</td>\n",
       "      <td>-0.071843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03252</td>\n",
       "      <td>-0.076908</td>\n",
       "      <td>-0.095927</td>\n",
       "      <td>-0.059296</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2992</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.13727</td>\n",
       "      <td>-0.070091</td>\n",
       "      <td>-0.094592</td>\n",
       "      <td>-0.047360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2993</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.14418</td>\n",
       "      <td>-0.124489</td>\n",
       "      <td>-0.171069</td>\n",
       "      <td>-0.072770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2994</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.25308</td>\n",
       "      <td>-0.154913</td>\n",
       "      <td>-0.213946</td>\n",
       "      <td>-0.082722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2995</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>4.00</td>\n",
       "      <td>1.67025</td>\n",
       "      <td>-0.159513</td>\n",
       "      <td>-0.229822</td>\n",
       "      <td>-0.088553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2996</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dydx</td>\n",
       "      <td>4.25</td>\n",
       "      <td>0.29633</td>\n",
       "      <td>-0.173986</td>\n",
       "      <td>-0.238259</td>\n",
       "      <td>-0.101553</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2997 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         term estimate_type  educ4  dist100  estimate  lower_3.0%  upper_97.0%\n",
       "0     arsenic          dydx   0.00  0.00591 -0.086666   -0.111418    -0.065328\n",
       "1     arsenic          dydx   0.00  0.02409 -0.097186   -0.126608    -0.070509\n",
       "2     arsenic          dydx   0.00  0.02454 -0.056941   -0.065824    -0.048077\n",
       "3     arsenic          dydx   0.00  0.02791 -0.098558   -0.128713    -0.071843\n",
       "4     arsenic          dydx   0.00  0.03252 -0.076908   -0.095927    -0.059296\n",
       "...       ...           ...    ...      ...       ...         ...          ...\n",
       "2992  arsenic          dydx   4.00  1.13727 -0.070091   -0.094592    -0.047360\n",
       "2993  arsenic          dydx   4.00  1.14418 -0.124489   -0.171069    -0.072770\n",
       "2994  arsenic          dydx   4.00  1.25308 -0.154913   -0.213946    -0.082722\n",
       "2995  arsenic          dydx   4.00  1.67025 -0.159513   -0.229822    -0.088553\n",
       "2996  arsenic          dydx   4.25  0.29633 -0.173986   -0.238259    -0.101553\n",
       "\n",
       "[2997 rows x 7 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# average by both educ4 and dist100\n",
    "bmb.interpret.slopes(\n",
    "    well_model_interact,\n",
    "    well_idata_interact,\n",
    "    wrt=\"arsenic\",\n",
    "    conditional=None,\n",
    "    average_by=[\"educ4\", \"dist100\"]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is still possible to use `plot_slopes` when passing an argument to `average_by`. In the plot below, the empirical distribution is used to compute unit level slopes with respect to `arsenic` and then averaged over `educ4` to obtain the average predictive slopes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AVX079p9rh9nqRlV9B6rqO5CdrsC8kizMLcqAVsWC1jT6giLQYveixe7FcZMVEgEwaJThnkMW8KaR4vIG0GL3oN3phdXlx9hMNYqy0uJqBulw1Lc5cdxkRVu3O0QUHV5/EIfrOvBFow2zx2agJM6Wt4tpGNywYcOAgWn//v0A0GtvlCiKI9JLdel5h3LtdevW4eGHHw6/tlqtKCwsjG5DR5BaIUXZBAMWjs/GhQ4X9p9rw5HzFrQ6vPif42bsOGHGlDwd5haFxkMIQmhWqYDOj50zTgWEnkvC2y9uu/S5BOh2nv6fE3UJigivDf35hVDtr2xNqOfQqFPCkK5Mml/WvXH7AvD4gnD7A3B5A3D7A3D7gqHt/iDkEgFy2cWalHJpZFmh7vvoIrcvgDaHF20OL1odXrQ5PHB5I8fJ1bU5caiuHRNyNJho1CTkGuSiKKK21YkTDVZ0OH0Dv4GGxeEJYPfZVpwy2zCvJBOGOKn7G9Pv3NWrV2PlypX9HlNSUoKjR4+isbGxx77m5mbk5uYO+fp5eXnwer1ob2+P6B1samrCokWLwscM5dpKpRJKZXx8kYdDEASMzUzD2Mw0fHNmPj4/b8H+c22ob3fhRIMVJxpG//Z36DZhaJUOY+dav1y1g7r4gyIarR40WkO3uGQSIfS9ogv1HmanK+L6DwpRFOHxh8Kcy3cx2Lkvfe4PhcBoDeWKLDEkdH6UhutSyqVCuGalXCb0CJfx/G86EK8/iHZnaChCKPxd/uxYty+I46bQz8KCDDUmGTUoyFCPcIuHLxgUUdPqwAmTFTa3f+A3UFS1Obw402RnGAQAg8EAg8Ew4HFlZWWwWCzYt28f5s+fDwDYu3cvLBZLOLQNxZVXXgm5XI4dO3bgtttuAwA0NDTg2LFjeO6550b02olIKZNiXkkW5pVkwWxxh+sWBkQRoihCFAERCD8PdnsuovNjX88H0Y7QbUIPWuwenGyI3KdTyULhUKtEjlYVXvtXq5Il9C8rGjp/UESDxY0GixuABTKpEBpnKJGE6yIK3eojdj3vqpsodD7vXmOxx/5L6ilG7JeEngtAOOSFg50/AHcvvXmxmNYniqFQNNRVIeSdAVIpk0AplyJNLkWaQga1Qgq1IvRarZDG/A82fyCINqcX7Q4fWu0etHXe8h0uUQQutLtwod0FjUqGiTkajM9Jj/nne6lAUMTZZjtONlhZDobCEmI2MRAq72IymbBlyxYAofIuxcXFEeVdpkyZgk2bNuGWW24BALS1taGurg4mkwnLly/HO++8g9LSUuTl5SEvLw9AqLTMBx98gDfeeANZWVl45JFH0Nra2qO0zEDXHkg8zyaOB6IodgbJfoKjKCIIwBeIrGzf9bB5+v6BrpRJLvYkajqDolaJrHQFx5cRjSKJEBqGou4eFuVSpClCj67X0ZgUFAyKaHd2v9XrhWUUiyFLJUBRVjom5Wpi3gPkDwRxusmOU2Zrj9vdFBvjc9KxcHz2iJ0/6WYTA8Bbb72FNWvWhGf+rlixAps3b444prq6GhaLJfx627Zt+MEPfhB+3XVLev369diwYQMA4Pnnn4dMJsNtt90WLjr9xhtvhIPg5V6bhqdr/GC3//QrM02BSUZtxDaXNxBe87K5sxJ+k82DNocXHn8Q59tdON/uiniPVBCQpVF0C4mhwscGrYKFj4lGQFAMjZsK9Ur1PVFBLhWQppAhrbM38dKwmKaQQSW/eHtaFEVYXL5w6Gu1e9Hh9EbtNvpQBIJATYsDNS0OZKXLMdGoRUl22qjOfvf6g/ii0YZqs421AalPCdMzmOjYMxg7/kAwXDC1yRa6vdy1bJIv0Pe3v14tD99yNmqVyNOpkKdXMSQSxQmJAKjkUihlEtjc/oSohSeXChifk46JRi306pGryOD2BVBttuGLRlu/P+codtgzSDSKZFJJuCZdd0FRhNXlCy+n1FVdv8nmgcPjh8Xlg8Xlw5mmiwFbAJCtUSBfr0a+XhX6mKGCVskxiUSjLSgCTm8ATm/ijH3zBURUm+2oNtuRq1NiklGLsZnqqM14d3kDONFgxdkme0KEY4oPDIOUsiSCgIw0BTLSFJicG3nL2en1R4xHbLS5Yba4YXX7wzXuPr9wcUhCulKGgm7hMF+vgkGjhIQBkYj60DXrXa2QhMvTDHW1J4fHjxMNVnzZbEeAd4NpkBgGiXqRppChOFuG4uzIwqB2jx8NFhcaOtyhjxZ35yLtfpxusuN0t15EuVRAnu5iQCzQq5GrU7GeGxFFcHmDOHbBihOmUHmaybla5OlVA78RgNXtw/ELVtS2OmI6PpISG8Mg0SBolDJMMmojJq94/UE0Wt2d5Utc4Y++gIj6dhfqu01aERBaLSPUe9h1q1nF1VyICEER4YluWpUMk3JDK0L19gdkhzO0Ak9dmzMmpYgouTAMEg2TQiZBYVYaCrPSwtuCoohWuzciHDZ0uGHz+EMznu0eHD1/8TazVimLCIgFejWyNAreZiZKUTa3H4dqO3C03oKi7DRMztUiK12BVrsHx0xWXLikMgLRcDAMEo0AiSCEZyLPGntxu83tC4XDDhdMnYWQW+2hGom2Rju+aIy8zZzeWYetq7SGWt756FZiQ91te5pCBqVcwhBJlCT8QRFfNjvwZbMD6UopC0XTiGAYJBpFWpUcWpU8YsKK1x+E2eqOGItotrrhC4jocPnQ4RrceqECAKVcEiroK78kMPbzMU0eWnqMs6KJ4hODII0UhkGiGFPIJCjKSkNRt9vMgaCIDqcXTm9ofVqXLwCXt9vHzudObyC8hq3T64cvEFrJJbS0Wd8FffsiEboCqww6lRw6deijViWHTiWDVh36qJZLGRqJiJIEwyBRHJJKBGRrlBhsOVJ/MBgRFi8NkU5faC3criDZfX8gKCIoIlxfEeh7TJJMIoQDY1dA1HWFSPXFMKlkTyMRUdxjGCRKIjKJBFqVZNCzk0VRhC8gwun1w+b2w+b2wer2w+r2webq/Nj52ukNwB8U0e70od3Z/y1shVTSIyBe7GHsfK6Ss9wOEVEMMQwSEQRBgEImQCELFeHujy8QhL0zGFq7gqOrK0Be3Ob2BeHtXAqw1dH3LWsBQFFWGqYX6DCtQI+s9P6vT0RE0cUwSESDIpdKkJmuQOYAoc3rD17Sw9jtudsPqysUHn0BEbVtTtS2OfHhMTMK9CpMK9BjeoEORq2St5mJiEYYwyARjQiFTBIa96hR9nmMKIqwuHw42WDFcZMVNS0OmCxumCxu/PVkIwwaJaYX6DC9QIcxGWoGQyKiEcAwSEQxI3SuD102wYCyCQY4PP5wMDzTbEeL3YNdXzRj1xfNyFDLMa1Ah+kFehRnp7GWIhFRlDAMElHcSFfKMK8kC/NKsuD2BVDdaMNxkxVfmG3ocPmw+2wrdp9tRbpCGg6G43PSIZNwAgoR0VAxDCYR9pRQMlHJpZg9NgOzx2bAFwjiTJMdx00WnGywweENYP+5duw/1w6VXIIpeTpMy9dhcq6WM5OJiAaJYTCJXFGUiYIMNWpaHKhrc8If4OrllBzkUgmm5uswNV+HQFBETYsDx00WnDBZYfP4UVXfgar6DsilAiYZtZheoMOUPB3UCmmsm05EFPcEURSZGEaB1WqFXq+HxWKBTqcb8ev5A0Gcb3ehpsUBs9UNfpUpGQVFEfVtThw3WXHcZImoeygRgAk5Gkwv0GNqvnbQtReJiEbS+Jx0LBw/2KUFLt9gcgfD4CgZ7TDYncsbwLlWB2paHOgYoEgwUaISRRENFnc4GDbZPOF9AoDi7DRML9BjWoEOmQPUUiQiGmkMgykolmGwu3aHF1+2OFDb6oDbF4xZO4hGWrPNgxMmC443WHG+PXJpvQK9CjlaJXRdK6F0LanXuVIKJ6QQ0UhjGByC9vZ2rFmzBtu2bQMArFixAr/4xS+QkZHR53vef/99bNmyBQcPHkRraysOHz6MOXPmhPe3tbVh/fr1+Oijj1BfXw+DwYCbb74ZTz75JPR6ffi4kpIS1NbWRpz70UcfxTPPPHPZ7Y+XMNhFFEWYLG6ca3HgQrsL/mBCfBsQDUmH04sTnSVrzrU4MNB3e7pSBn1nOAyFRVm30Bh6rZZLWfeQiIYsnsJgwkwgueOOO3D+/HlUVFQAAO655x6Ul5dj+/btfb7H4XDg6quvxq233opVq1b12G8ymWAymfDv//7vmDZtGmpra3HffffBZDLhvffeizj2iSeeiDiHRqOJ0mcWG4IgYEyGGmMy1PD6g6hrc+JciyPi1lo8EYTQL+gMtRwalQyBoAifPwhPIAifP7TsmS8QhNcfRIAdnnSJjDQFFk0wYNEEA+weP75stsPi8nWugnJxNRSr249AUITD44fD44fJ4u7znHKpAK3qYjjUd4XFrl5GlRxaNXsZiSj+JUQYPHnyJCoqKlBZWYkFCxYAAF599VWUlZWhuroapaWlvb6vvLwcAHDu3Lle98+YMQN//OMfw68nTJiAp59+Gt/73vfg9/shk13859FqtcjLy4vSZxRfFDIJJho1mGjUwO7x41xLaHyhze2PSXvSFFLo0+TQq+XIUMuRkaaATiWDTHp5v1QDQRHeSwKi19/5POK1CF8gCE/XsZ3HsJM0uWmUMswam9HrPlEU4fAGLoZDV+cazJe8dnoD8AVEtDm8aOtn3WUASFdIw7efpV09iYKA7n2K3TsYhW4bBzqme89kb8dKBAF5ehWKstKQr1dDKmFPJhH1lBBhcM+ePdDr9eEgCAALFy6EXq/H7t27+wyDQ9HVndo9CALAs88+iyeffBKFhYW49dZb8X//7/+FQtH3IHSPxwOP52Ivm9VqjVobR5JGKcOMMXrMGKNHs82Dc60O1LY64fVHv7tNKZMgoyv0pYV6VDLUimHXiZNKBKgVUqgxtLIivu4hsvOjxx+E1eVDR2dvksMTGFYbKT4JggCNUgaNUoYCqPs8zhcIwub2h3oXu8JiX72M3gAc3gAaLKP4ifRCJhEwJlONoqw0FGamoSg7DTrOsCYiJEgYNJvNMBqNPbYbjUaYzeaoXae1tRVPPvkk7r333ojtDz74IObOnYvMzEzs27cP69atQ01NDV577bU+z7Vp0yZs3Lgxam2LhRytEjlaJa4sysSFDhe+bHGgocM16J4zmVSI6OXrCn8qeXzWgJNLJZBLJehvwqnXH4TF5et8eNHhDD3npJzUIJdKkJWuQFZ6398kXb2Mts6waHP7IYqACEDsNmqx+6htsdtGMeJcQzvG6w/iQocT9W0uuHwB1LY6UdvqDB+XkSZHUVZa+JGnV/G2NlEKimkY3LBhw4CBaf/+/QDQ60BtURSjNoDbarVi+fLlmDZtGtavXx+xb+3ateHns2bNQmZmJr7zne/g2WefRXZ274M/161bh4cffjji/IWFhVFp62iTSAQUZqWhMCsN7s5fKDUtjh63x6QSQKeSX7zFm6ZAhlqOdGVC/M0xKAqZJByWu3P7AuGQ2BUQO5xe+FgAPOV072XM1/fdyzgaRFFEi92LujYn6tqcqG9zotHqRofThw6nBUfPh7otZZLQWOKizv/f2XtIlBpi+lt69erVWLlyZb/HlJSU4OjRo2hsbOyxr7m5Gbm5ucNuh81mww033ACNRoOtW7dCLu//h9/ChQsBAGfOnOkzDCqVSiiVyl73JTKVXIrSPC1K87SwuHxosLiQrpCFB82n+uxKlVwKlVyKXJ0qYrvT6w8HxK6QaHX5OIubRoUgCBd7+oszAYT+cDnf7gqHw7o2Z6j3sM2J2rZuvYdqeSgYdj7yM9h7SJRsYhoGDQYDDAbDgMeVlZXBYrFg3759mD9/PgBg7969sFgsWLRo0bDaYLVacf3110OpVGLbtm1QqVQDvufw4cMAgPz8/GFdO9Hp1aEeQBpYmkKGNEXPHiKb+2IvYvcxicyINNJUcml44hgQ2XvYFQ4brW50uHzouGDB5xciew+7B0Qdfw4QJbSEuH83depU3HDDDVi1ahW2bNkCIFRa5sYbb4yYPDJlyhRs2rQJt9xyC4BQHcG6ujqYTCYAQHV1NQAgLy8PeXl5sNlsWLp0KZxOJ37729/CarWGJ3rk5ORAKpViz549qKysxJIlS6DX67F//36sXbsWK1asQFFR0Wj+M1AS0qrk0KrkGJt5cVswKMLmCU1ECM1uFuEPigh0Pro/73odDG8P9tgfCIoMlzSg3noPPb4A6ofQe1iQwZnLRIkkIcIgALz11ltYs2YNli5dCiBUdHrz5s0Rx1RXV8NiuThlb9u2bfjBD34Qft11S3r9+vXYsGEDDh48iL179wIAJk6cGHGumpoalJSUQKlU4t1338XGjRvh8XhQXFyMVatW4Sc/+cmIfJ5EEokQ9V7XYFBEQOwZJv3BIIJBwB8Mhmo3BkS4vAE4vH44vX44PAG4vAHezk5Ryl56D1u7jT3sq/dQJZdgQk7ofZONWmT2M9GGiGIvYVYgSXTxtgIJ0WC4fQE4vQE4PP7QR68fTs/F0OjychZ1qvL4AjjfEeo9rGu92HvYnUGjwESjFpONGozLSYdSFp+VBIhGE1cgIaKE0jUxpq9SKsGg2BkMuwXGS4IjexeTk1IuxYQcDSbkhHoPg6KIC+0unG6y4XSTHfVtTrTYvWixt6Lyy1ZIBQFF2WmYbNRgYq4W+XoVJCk+8Ywo1tgzOErYM0iprq/eRas7NIGGkpPbF8DZZjtON9lxutGG9ku+1ulKGSZ13oqeZNRAy1I2lCLYM0hEKae/3sVGqxvHLljQaI3PtbFp6FRyKaYX6DG9QB8ac+jwhoPhly0OODx+VNV3oKq+AwCQp1NhUq4Gk4xaFGenQX6Zy1AS0dAxDBJRzOXqVMjVqdBi9+DYBQtMHe5YN4lGgCAIMGiUMGiUKBufDX8wiLo2J0432nGmyY4LHS6YrW6YrW787XQL5FIB4wzpmGTUYpJRgxytMuVrmRKNBN4mHiW8TUx0+dodXhw3WVHf7gR/QqUOu8ePs0328HhDm9sfsV+vlmOSUYNJuVpMzNFArRjeRBR/MAi3Lwi3L9D5CMIVfn5xW9dzV9dzfwCiCCyakI2vTDQwoNKQxNNtYobBUcIwSDR4VrcPJ0xWnGtxsFZiihFFEY1WTygYNtpxrtURMQlJADA2U41JuVqMz0mHBALc/sgA57okzF26LxqTmuaXZOFbswtYV5EGjWEwBTEMEg2dw+PHyQYrzjbbEWAVm5Tk9QdxrtWB042hXsMmW/TGlyplEqjkUqjlUijlEqg7x7eq5KHtKlnPfedaHag4ZoYIYHKuBv94VRGUcpbMGSqvP4hGqxtmixtNNjd8QRGiGCqY3/UxeOnroAgR3fYFu/aJEHFxW1AUIXYeIyL0vu7bgiIgCMD10/Jw1bisUfucGQZTEMMg0fC5vAGcMltxuskOf4A/ulJZh9OLM02hWcp1bU7IJEJkgOst3MmkUCkk4XCn6tw/1NI2J0wWvHugHr6AiAK9CneWlXBpvgGIoogOlw9mixsNFjfMFhcaLG60ObyI9f/RcqmAh66bPGpF0hkGUxDDIFH0ePwBfGG2o7rRBq+fXYUUO/VtTvzXnnNweAPQq+W4q6wEefqB17hPBd17+xqsoeBntrrh9vX+/6xGKUOeXoVcrRIquRSCIEAiAJLOj0LEx4v7hEs+Xnq8RNK5D91fdzsGwJ+PmFDT4kBprhZ3lhWPyjjQpAmDXq8XTU1NCAYjv7Bcs7cnhkGi6PMFgjjdaMcps7XPXzBEI63N4cUbu8+hxe6BUibBdxcUh5fwSwW99faZrW602nvv7ZN2roOdp1chX69Cnl6FPJ0qpjUmm2xu/GLnGQSCIv5xfhFmjtGP+DUTPgyePn0a//RP/4Tdu3dHbBdFEYIgIBAI9PHO1MUwSDRyAkERZ5vtONlghcPDnz80+pxeP35bWYdzrQ5IBOCWK8biyuLMWDcr6sK9fdau4OeG2erq84+xdKUsFPh0F4NfjlYJmST+6kf+9WQjdp5qglYpw9pvTIZqhMeAxlMYHFKdwe9///uQyWT44IMPkJ+fz2n1RBRTUomAyZ3lRmpaHThhsvYoS0I0ktIUMvzT1SV479B5HD1vwR8PnUe704vrphgT9ndkUBTD9R+7gl+r3dNrb59EAIzai718XcEvkVaUuXZyDo7Ud6DV4cX/HDfjpjljYt2kUTOkMFhVVYWDBw9iypQp0W4PEdGQSSQCJuRoMN6Qjro2J46brFzqjkaNTCrBbfMKkZWmwCdfNGPnqSa0O7y4Ze6YuOwJ609dmxPbqi7AZOlZAD5dIUW+Xh1xmzdee/sGQy6V4OYrxuD1z2qwr6YNc4syUZiVFutmjYohhcFp06ahpaUl2m0hIooKQRBQnJ2O4ux0nG8PhcJWuzfWzaIUIBEELJ2eh8w0Bf585AIO13fA4vLhuwuKh10kezQ4PX5UHDfjQG07AEAll2BKni5he/sGa0KOBlcUZuBwfQe2Hr6AHy2ZmBI1JC97zKDVag0/P3DgAB5//HH87Gc/w8yZMyGXR35jcExcTxwzSBRbZosbx01c/5hGzxeNNry9rw5efxBGrRJ3LSpBZtrolC0ZrKAo4mBtOyqOmeHyhcbdzi3KxA0z8qBRptbKtXaPH8/v+AIuXwDLZuThmkk5I3KdeBozeNlhUCKRRIx76Jos0h0nkPSNYZAoPjTbPDhltsJsccPHWoU0whosLvxm9zlY3X5olTKUlxVjbGZ83Xo0dbjw56oLqG93AQDydCqsmF2AEkN6jFsWOwdr2/DHQxdGtPZgPIXBy477H3/88bAbRkQUazlaJXK0ob/0O5xeNNs8oYfdw5nIFHX5ejXuXzwRv9l9DmarG6/+7Uv841VFmJIf+04Bty+AHScaUfllK0QACpkEX5+ai7Lx2Slxa7Q/c4sycbC2A+daHdh2xDRqtQdjhUWnRwl7Bonin8PjDwfDZpuHk08oaty+AN7eV4czTXYIAL41u2BEe4X6I4oiquo78JdjZtg9oVn3M8fo8c2Z+dBzBZWwJmtn7UFRxB3zizAjyrUHE7JnsLtf//rX0Gg0uPXWWyO2/+EPf4DT6cRdd901lNMSEcVUulKGdKUsfHvM6w+Gg2GzzYM2h4drI9OQqORS3FVWgj9VXcDB2nZsO2JCu8OL62fkDXk5vKFotLqxrXO1DQAwaJRYMbsgpYpkXy6jToWvTs7Bx9VN2H7UhIlGzYjXHoyVIYXBZ555Bi+//HKP7UajEffccw/DIBElBYVMgjEZaozJUAMIFbdudVwMhy12L5fDo8smlQj49hVjkJWuwI4TjfjbmRa0O724dV4h5NKRLcvi8Qew81QT/n6mBUExtA7vklIjvjLRANkIXzuRLS7NwdHzodqDH51oxIrZBbFu0ogY0ndAbW0txo0b12N7cXEx6urqht2o3rS3t6O8vBx6vR56vR7l5eXo6Ojo9z3vv/8+rr/+ehgMBgiCgKqqqh7HLF68GIIgRDxWrlw57GsTUfKRSgQYtSpML9BjcakR37lyLL45Mw9XlWSiJDsN6crk7DWg6BGEUAi7bd5YSAUBx0xWvP5ZTfh2bbSJoohjFyx44a+n8bfToSA4NV+Hh66bjMWlRgbBAcilknDx6b1ftqK+zRnjFo2MIX0XGI1GHD16tMf2I0eOIDt7ZO5/33HHHaiqqkJFRQUqKipQVVWF8vLyft/jcDhw9dVX45lnnun3uFWrVqGhoSH82LJly7CvTUSpISNNgUm5WiyaaMBNc8bgpjkFWDQhG5NyNchIkyOJx5zTMMwpzMQPri6BSi5BXZsTW3adRYs9umWPWuwevLH7HN7eVweLy4fMNDnuXFiM8oXFIzI7NllNNGowpzADIoA/VV1AIJh8Uy2GdJt45cqVWLNmDbRaLb761a8CAHbt2oUHH3ywR69aNJw8eRIVFRWorKzEggULAACvvvoqysrKUF1djdLS0l7f1xXYzp071+/509LSkJeXF9VrE1Fq6mvcYavdA5c3AI8/CI8/CK8/CI8/AK8/iCT83UKXYXyOBvd9dQJ+s+ccWh1evLzrLMoXFqM4e3glXXyBIHZ90YxdXzQjEBQhlQj46qQcLC7NGfHb0cnqmzPzUW22ocHixu6zLSNWezBWhhQGn3rqKdTW1uK6666DTBY6RTAYxJ133omnn346qg0EgD179kCv14fDGAAsXLgQer0eu3fvHnYge+utt/Db3/4Wubm5WLZsGdavXw+tVjusa3s8Hng8F//K6160m4hSx6XjDnvTFQy7gqLHF+gWGDv3+TpDZCD0nAEyORh1Ktx37QT8155aXOhw4fXPanDrvELMHOLM1VNmK7YfMaG9cyb8JKMG35pdAINGGc1mpxyNUoZlM/Lw/uEL+OvJRswYo4/bAuJDMaQwqFAo8O677+Kpp55CVVUV1Go1Zs6cieLi4mi3DwBgNpthNBp7bDcajTCbzcM693e/+12MGzcOeXl5OHbsGNatW4cjR45gx44dw7r2pk2bsHHjxmG1jYhSg0ImgUImgXYQ7+keIHsNjf4gHF4/l+FLAFqVHKuuGY9399fhpNmG3+2rQ/v0PFwzyXDZte3anV58cLQBJxtCHQ86lQzLZxVgRoEuqevjjaa5xZk4VNeOc61ObD9iQvnC5Kk9OKQw+MQTT+CRRx7BpEmTMGnSpPB2l8uFf/u3f8NPf/rTyzrPhg0bBgxM+/fvB4Be/8F7WwVlsFatWhV+PmPGDEyaNAnz5s3DoUOHMHfu3CFfe926dXj44YfDr61WKwoLC4fVViKiLpcbIK1uH8402fFls4Mzn+OYQibBdxcW47+PNmDPl62oOG5Gu9OLG2cV9FsA2h8M4rPTLfi4ugm+gAiJAFw9wYCvTTFCmaRlUGJFIgi4ac4YbN55BqfMNhw3WaNeezBWhhQGN27ciPvuuw9paZFL6jidTmzcuPGyw+Dq1asHHGNYUlKCo0ePorGxsce+5uZm5ObmXn7DL8PcuXMhl8tx+vRpzJ07F3l5eUO6tlKphFLJbnkiii2dSo65RZmYPTYDdW1OnG60oYW9hXFJIgi4cVY+stIV+PDzBuytaUOH04eV8wuhlPUMdmea7Nh2xBSeeFKSnY6b5hQgV6ca7aanjFydCtdMNuCT6mZ8kES1B4cUBvvqFTty5AiysrIu+zwGgwEGg2HA48rKymCxWLBv3z7Mnz8fALB3715YLBYsWrTo8ht+GY4fPw6fz4f8/PxRvzYR0UiRSgSMM6RjnCEdHU4vTjfZUdPigJ/rM8cVQRBw9UQD9Go5fn+gHtWNNrz6ty9xZ1kJdKrQ6iBWlw8fHmvA0fMWAKFJS9+ckYc5hRlJc9syni0pNeLoeQvaHF7sONGIbyVB7cFBLUeXmZkJQRDCS5t0/6YLBAKw2+2477778Mtf/jLqDV22bBlMJlO47Ms999yD4uJibN++PXzMlClTsGnTJtxyyy0AgLa2NtTV1cFkMmH58uV45513UFpairy8POTl5eHs2bN466238M1vfhMGgwEnTpzAj3/8Y6jVauzfvx9SqfSyrz0QLkdHRPHGFwiittWB04328IQDih91bU78155zcHoDyFDLUV5WjC+bHfjryUZ4/EEIABaMz8Y3puZCrUj83qlEcrrJhl///RwEAPcvnoCxmWkDvudS8bQc3aDC4G9+8xuIooh/+qd/wgsvvAC9/uK9coVCgZKSEpSVlQ295f1oa2vDmjVrsG3bNgDAihUrsHnzZmRkZISPEQQBv/71r/H9738fAPDGG2/gBz/4QY9zrV+/Hhs2bEB9fT2+973v4dixY7Db7SgsLMTy5cuxfv36iB7Oy7n2QBgGiSietdg9ON1oR12bg0vuxZHWzlqBrY7IW/uFmWqsmDOm31nqNLJ+f6AeVfUdKNCrcP/iif2O7exNwobBLrt27cKiRYsgl3NB68vFMEhEicDjD6CmxYEzTXZYXSOzKgYNjtPjx5t7a1Hb6oRaLsUN0/NwZUnmqK5pTD3ZPX48v+MLuHwBfHNmPr4yceBhb90lZBi0Wq3hkw1UM49hpyeGQSJKNI1WN0432nG+3cm6hjHmDwRxusmO4qw0pCmHNNyfRsD+mjZsrboAhVSCh74+CRmDqD0YT2Hwsr+jMjMz0dDQAKPRiIyM3gepdk0sCQQCg281ERHFlVydCrk6FVzeAM4223G22Q6Hhz/fY0EmlWBqPjsS4s2VJaHag7VtnbUHy0pi3aQhuewwuHPnzvA4up07d3LGEhFRilArpJgxRo/pBTqYLG6cbgwtyzX4QUZEyUUiCLj5ijH4xc7TOGm24YTJgmkFiVd78LLD4LXXXht+vnjx4pFoCxERxTFBEMJL6zk8/lAx6xY7XF7OOKHUlatT4auTcvDJF83YdsSECTmahCv4PaQVq6+++mr88z//Mz766CM4HI5ot4mIiOJculKG2YUZuGn2GHxlogG5OhbZp9S1ZIoRWekKWN1+7DjZc6GKeDekMHjjjTfi0KFD+M53voPMzEyUlZXhscceQ0VFBex2e7TbSEREcUoiEVCUnYbrpuZi+ax8lOZpIBtkiQ2iRCeXSnBTZ/HpPWdbcb7dGeMWDc6QSst0CQQC2L9/Pz755BN88skn4bGEHo8nmm1MCpxNTESpwu0L4JTZhi8abVzhhFLKO/vrcPS8BQUZKtx/bf+1B+NpNvGQega7nD59GkeOHMGRI0dw9OhR6HQ6fPOb3xzOKYmIKMGp5FLMKczATXMKMGOMDnIpewopNSyfmQ+VXAJThxuVX7bGujmXbUhh8Pbbb0d+fj6uvfZa/PWvf8WiRYtQUVGBlpYWbN26NdptJCKiBKSUSTFrbAZumjMGs8bqoZQNq/+BKO5pVXLcMD0fALDjZCM6nN4B3hEfhlS58g9/+AMMBgO+//3vY8mSJbjmmmug0Wii3TYiIkoCCpkEM8boUZqnxelGO06ZrXD7OAOZktO8ztqDdW1OfHC0Ad9bWBzrJg1oSH+mtbW14bXXXoPf78fjjz8Og8GABQsW4NFHH8Vf/vKXaLeRiIiSgFwqwbQCHVbMLsDc4gyoFewppOTTVXtQIgAnGqw4YbLEukkDGtYEki5nz57FU089hd/+9rcIBoNcgaQXnEBCRBQpEBTxZbMdJxqsXNmEks7/HDdj1xfN0KvleOi6ST1qD8bTBJIh3SZua2vDrl27wrOIjx8/jqysLNx0001YsmTJkBpNRESpRSoRMClXiwk5GnzZ4sCJBivsbn+sm0UUFUtKjTh6vgPtTh/+erIRy2cVxLpJfRpSGMzJyYHBYMA111yDVatWYfHixZgxY0a020ZERClAIhEw0ajBeEM6zrU6cNxkhY2hkBKcQibBTXPG4I3d57D7bCvmFGViTIY61s3q1ZDC4OHDhzF+/PjwpJHa2lq88MILmDZtGpYuXRrVBhIRUWqQSASMz9FgnCEddW1OHLtghcXli3WziIZscq4Ws8bqcfS8BX86fAH3L54AiRB/pZaGNHr3kUcewW9/+1sAQEdHBxYsWID/+I//wE033YSXXnopqg0kIqLUIggCirPTsXxWPq6ZZEBmmjzWTSIasq7agxc6XHFbe3BIYfDQoUO45pprAADvvfcecnNzUVtbi//6r//C//t//y+qDSQiotRVmJWGZTPz8dXJBmSlK2LdHKJB06rkuH56HgDgoxONcdnbPaQw6HQ6odVqAQAfffQRvv3tb0MikWDhwoWora2NagOJiIjGZqbhhhl5WDIlBzlaZaybQzQoV5VkoTBTDa8/iO1HTLFuTg9DCoMTJ07En/70J9TX1+N//ud/wuMEm5qaWDaFiIhGTL5ejW9My8V1U43I1TEUUmKQCAJuuWJsuPbgyQZrrJsUYUhh8Kc//SkeeeQRlJSUYMGCBSgrKwMQ6iW84oorotpAIiKiS+XqVLhuai6+Ps2Iwiw1crRKZGsUyEpXIDNNjow0OXRqGTQqGdKVUqQppFArJFDKJFDIJJBJBcgkAiTxN5afklSeXoWvTMwBAGw7YoLbFz+1NYdcdNpsNqOhoQGzZ8+GRBLKlPv27YNOp8OUKVOi2kgAaG9vx5o1a7Bt2zYAwIoVK/CLX/wCGRkZfb7n/fffx5YtW3Dw4EG0trbi8OHDmDNnTnj/uXPnMG7cuF7f+/vf/x633norAKCkpKTH7e9HH30UzzzzzGW3n0WniYjilyiKEEUgKIoIioCI0OuubSI6P4qhY21uP/5+pgXBYS/bQKnE6w/iP//3C7Q7fVg+Mx+//O7cEbvWYHJHVFYgGQ3Lli3D+fPn8corrwAA7rnnHpSUlGD79u19vufNN99ETU0NCgoKsGrVqh5hMBAIoLm5OeI9r7zyCp577jmYzeZw6ZySkhLcfffdWLVqVfg4jUYzqPWYGQaJiJLLcZMFR+rjf6kxii/VZht+s+ccBAHYvvormDFGPyLXGfEVSEbbyZMnUVFRgcrKSixYsAAA8Oqrr6KsrAzV1dUoLS3t9X3l5eUAQj2AvZFKpcjLy4vYtnXrVtx+++09gp5Wq+1xLBERpa7pBXo0WT1osLhj3RRKIKV5Wswco4dWJYMxTsa9JsQq4Xv27IFerw8HQQBYuHAh9Ho9du/eHbXrHDx4EFVVVbj77rt77Hv22WeRnZ2NOXPm4Omnn4bX6+33XB6PB1arNeJBRETJpWxCNtSKhPhVSnHktnmFWHPdJBi1qlg3BUCC9AyazWYYjcYe241GI8xmc9Su8/rrr2Pq1KlYtGhRxPYHH3wQc+fORWZmJvbt24d169ahpqYGr732Wp/n2rRpEzZu3Bi1thERUfxRyaVYNMGAnaeakBiDrigeSONs5lJM/5zZsGEDBEHo93HgwAEAoYr0lxJFsdftQ+FyufD222/32iu4du1aXHvttZg1axZ++MMf4uWXX8brr7+O1ta+K4mvW7cOFosl/Kivr49KO4mIKL7k6lSYUTAy476IRkNMewZXr16NlStX9ntMSUkJjh49isbGxh77mpubkZubG5W2vPfee3A6nbjzzjsHPHbhwoUAgDNnziA7O7vXY5RKJZTK+BgLQEREI2vGGB2abG40Wj2xbgrRoMU0DBoMBhgMhgGPKysrg8Viwb59+zB//nwAwN69e2GxWHrc0h2q119/HStWrEBOTs6Axx4+fBgAkJ+fH5VrExFRYhMEAYsmGPDh5w3w+IOxbg7RoCTEmMGpU6fihhtuwKpVq7BlyxYAodIyN954Y8RM4ilTpmDTpk245ZZbAABtbW2oq6uDyRRa+qW6uhoAkJeXFzEz+MyZM/j000/x4Ycf9rj2nj17UFlZiSVLlkCv12P//v1Yu3YtVqxYgaKiohH7nImIKLGoFVIsmpiNj081D3wwURxJmClQb731FmbOnImlS5di6dKlmDVrFt58882IY6qrq2GxXKz5tG3bNlxxxRVYvnw5AGDlypW44oor8PLLL0e871e/+hXGjBkTXlavO6VSiXfffReLFy/GtGnT8NOf/hSrVq3C7373uxH4LImIKJHl69WYVsBaspRYEqbodKJj0WkiotQQDIr468lGtNj7L0FGkSQCkJWuQLZGAY8vCKvbD5vbB18gOWPK+Jx0LBzf+7yDaEi6otNERESJQiIRcPVEA/5yzAwvxw/2SS4VYNAokaNVwqhVIitdAZm05w1Lty8AW2cwtHv84ec2tz9pg+JoYxgkIiKKsnSlDAvGZeFvp1ti3ZS4oVZIkKNRhcNfRpr8ssrDqeRSqORS5Gh7Vuhw+wKwun2wu7tCoh92jw9Wtx9+BsXLxjBIREQ0Agqz0lCap0G12R7rpsSETi1DTmfPX45WCa1KHvVrdAVFo7bnPpc3AJvHdzEkdutR9AcZFLtjGCQiIhohVxRmotnmQZvDF+umjCiJAGSmK0LBrzMAquTSmLZJrZBCregnKLpDPYh2jx/NNg+abalbI5JhkIiIaIR0jR+sOGZOqvFtMqkQ0euX3cd4v3gVDoqd8yrcvgD+XHUBgRQd4skwSERENIK0Kjnmj8vC38/0vYRpvOs+3i9Hq0TmZY73SxQquRTF2en4stkR66bEBMMgERHRCCvOTkej1YMzTYkzflAqAWaOyUBhlnpExvvFm9JcLcMgERERjZwrizPRYvegwxn/4wfTlVJcMykHWemKWDdl1HSNeUzFsYOJc4OfiIgogUk7xw/KJPF9e3VsphrLZuSnVBDsUprby2yTFMAwSERENEr0ajnmlWTGuhm9kgjA3OIMfHVyDhSy1IwHhVlqpCtjOws6FlLzq01ERBQj43M0GGdIj3UzIqQrpfj6tFxMyUvt5VIFQcBEoybWzRh1DINERESj7KqSTOjU8TFsvyBDhRtm5MGg6bnCRyqakKOJ+1v50cYwSERENMpkUgm+MtGAWJbmkwjA7EI9FpcaoZSl3q3RvoTKzKTFuhmjimGQiIgoBjLSFJhbFJvxg2qFBF+basT0An1Mrh/vSvNSayIJwyAREVGMTMrVoihrdHuh8vUqLJuRD6NWNarXTSQZaQoYtalz25xhkIiIKIbmj8uCRjXy4wcFAZg1Vo/FpTkxXzc4EaRS7yDDIBERUQwpZBJcPSEbIzlnQSWX4GtTjJgxRp9Uy8iNpLGZqVNmhmGQiIgoxrI1SswpyhiRc+fqlFg2Ix+5Ot4WHgxBEDDJmBq9gwyDREREcWBKng5jM9VRPef0Ah2+NsUItSI1eriibYIxPSXKzDAMEhERxYkF47OicmtSKZNgcWkOZhdm8LbwMChlqVFmJmHCYHt7O8rLy6HX66HX61FeXo6Ojo4+j/f5fHj00Ucxc+ZMpKeno6CgAHfeeSdMJlPEcR6PBw888AAMBgPS09OxYsUKnD9/fljXJiIiGgqlTIpFEwzDGj+Yo1Vi2cw8FGREt5cxVaXCRJKECYN33HEHqqqqUFFRgYqKClRVVaG8vLzP451OJw4dOoR//dd/xaFDh/D+++/jiy++wIoVKyKOe+ihh7B161a88847+Oyzz2C323HjjTciEAgM+dpERERDlaNVYubYodX/m5KvxXVTjEhTxMfqJskgI02BXF1yl5kRRFEUY92IgZw8eRLTpk1DZWUlFixYAACorKxEWVkZTp06hdLS0ss6z/79+zF//nzU1taiqKgIFosFOTk5ePPNN3H77bcDAEwmEwoLC/Hhhx/i+uuvj9q1rVYr9Ho9LBYLdLrUXvuRiIgG9vGpJjRY3Jd1rEImwcLxWRibmfy3NGOhvs2Jv51uieo5x+ekY+H47Kies7vB5I6E6Bncs2cP9Hp9OIwBwMKFC6HX67F79+7LPo/FYoEgCMjIyAAAHDx4ED6fD0uXLg0fU1BQgBkzZoTPO9RrezweWK3WiAcREdHlKpuQDbVi4F/T2RoFls3IYxAcQcleZiYhwqDZbIbRaOyx3Wg0wmw2X9Y53G43HnvsMdxxxx3hhGw2m6FQKJCZGbkcUG5ubvi8Q732pk2bwmMM9Xo9CgsLL6udREREQGiN3EUTDOhv/kdpngbfmJqLdCVvC4+kZC8zE9MwuGHDBgiC0O/jwIEDANDrbChRFC9rlpTP58PKlSsRDAbx4osvDnj8pecdyrXXrVsHi8USftTX1w94XSIiou5ydSpML+h5i08uFXDNJAOuLM6CJAVKn8SDZC4zE9M/JVavXo2VK1f2e0xJSQmOHj2KxsbGHvuam5uRm5vb7/t9Ph9uu+021NTUYOfOnRH3zfPy8uD1etHe3h7RO9jU1IRFixaFjxnKtZVKJZTK5B5wSkREI2/mGD2abR40Wj0AgKx0Oa6eaIBWJY9xy1KLUiZFiSEdZ5rssW5K1MU0DBoMBhgMhgGPKysrg8Viwb59+zB//nwAwN69e2GxWMKhrTddQfD06dP4+OOPkZ0dOVDzyiuvhFwux44dO3DbbbcBABoaGnDs2DE899xzw7o2ERFRNAiCgEUTDPjw8wYUZqXhyuJMSJO0hyreleZqkzIMJsRsYgBYtmwZTCYTtmzZAgC45557UFxcjO3bt4ePmTJlCjZt2oRbbrkFfr8f//AP/4BDhw7hgw8+iOjFy8rKgkKhAADcf//9+OCDD/DGG28gKysLjzzyCFpbW3Hw4EFIpdLLvvZAOJuYiIiGw+0LQCVP3kkMieJ/TzaGe2mHg7OJh+Ctt97CzJkzsXTpUixduhSzZs3Cm2++GXFMdXU1LBYLAOD8+fPYtm0bzp8/jzlz5iA/Pz/86D4L+Pnnn8fNN9+M2267DVdffTXS0tKwffv2cBC83GsTERGNJAbB+DA5N/kmkiRMz2CiY88gERFR4hNFEduOmODwBAY+uB/sGSQiIiJKQIIgJF3vIMMgERER0SBMyNEkVZkZhkEiIiKiQVDIJBiXkx7rZkQNwyARERHRICXTrWKGQSIiIqJB0qvlyNMnx+ISDINEREREQ5AsvYMMg0RERERDMCZDDY0qpou5RQXDIBEREdEQhMrMaGLdjGFjGCQiIiIaovEGDWTSxC4zwzBIRERENEQKmQTjDIldZoZhkIiIiGgYEn0iCcMgERER0TDo1XLk61WxbsaQMQwSERERDdPkvMTtHWQYJCIiIhqmRC4zwzBIREREFAWlCTp2kGGQiIiIKArGGdITsswMwyARERFRFChkEoxPwDIzDINEREREUZKIE0kYBomIiIiiRKeSIz8jscrMMAwSERERRVGiTSRJmDDY3t6O8vJy6PV66PV6lJeXo6Ojo8/jfT4fHn30UcycORPp6ekoKCjAnXfeCZPJFD6mra0NDzzwAEpLS5GWloaioiKsWbMGFosl4lwlJSUQBCHi8dhjj43Up0pEREQJrCBDDW0ClZlJmDB4xx13oKqqChUVFaioqEBVVRXKy8v7PN7pdOLQoUP413/9Vxw6dAjvv/8+vvjiC6xYsSJ8jMlkgslkwr//+7/j888/xxtvvIGKigrcfffdPc73xBNPoKGhIfx4/PHHR+TzJCIiosSXSEvUCaIoirFuxEBOnjyJadOmobKyEgsWLAAAVFZWoqysDKdOnUJpaellnWf//v2YP38+amtrUVRU1Osxf/jDH/C9730PDocDMlko1ZeUlOChhx7CQw89NOTPwWq1Qq/Xw2KxQKfTDfk8REREFP98gSD+dPgCfIHeY9b4nHQsHJ89YtcfTO5IiJ7BPXv2QK/Xh4MgACxcuBB6vR67d+++7PNYLBYIgoCMjIx+j9HpdOEg2OXZZ59FdnY25syZg6effhper7ffa3k8Hlit1ogHERERpQa5VILxOYlRZiYhbmibzWYYjcYe241GI8xm82Wdw+1247HHHsMdd9zRZ0JubW3Fk08+iXvvvTdi+4MPPoi5c+ciMzMT+/btw7p161BTU4PXXnutz+tt2rQJGzduvKy2ERERUfKZlKtFtdke62YMKKY9gxs2bOgxMePSx4EDBwAAgtCzorcoir1uv5TP58PKlSsRDAbx4osv9nqM1WrF8uXLMW3aNKxfvz5i39q1a3Httddi1qxZ+OEPf4iXX34Zr7/+OlpbW/u85rp162CxWMKP+vr6AdtJREREySNRyszEtGdw9erVWLlyZb/HlJSU4OjRo2hsbOyxr7m5Gbm5uf2+3+fz4bbbbkNNTQ127tzZa6+gzWbDDTfcAI1Gg61bt0Iul/d7zoULFwIAzpw5g+zs3u/3K5VKKJXKfs9DREREya00V4uGDnesm9GvmIZBg8EAg8Ew4HFlZWWwWCzYt28f5s+fDwDYu3cvLBYLFi1a1Of7uoLg6dOn8fHHH/ca3KxWK66//noolUps27YNKtXACf7w4cMAgPz8/AGPJSIiotRVkKGGTi2D1eWPdVP6lBATSKZOnYobbrgBq1atQmVlJSorK7Fq1SrceOONETOJp0yZgq1btwIA/H4/vvOd7+DAgQN46623EAgEYDabYTabw5M/bDYbli5dCofDgddffx1WqzV8TCAQABCavPL888+jqqoKNTU1+P3vf497770XK1as6HNGMhEREVGXeC8zkxATSADgrbfewpo1a7B06VIAwIoVK7B58+aIY6qrq8MFo8+fP49t27YBAObMmRNx3Mcff4zFixfj4MGD2Lt3LwBg4sSJEcfU1NSgpKQESqUS7777LjZu3AiPx4Pi4mKsWrUKP/nJT0bi0yQiIqIkM86QjiP1HX2WmYm1hKgzmAxYZ5CIiCh1Haxti5hZzDqDRERERClkcq4Wl1EAJSYYBomIiIhGmFYlR74+PsvMMAwSERERjYLSvPicSMIwSERERDQK8vWhMjPxhmGQiIiIaJSUxmGZGYZBIiIiolEyzpAOuTS+ZpIwDBIRERGNEplUgvE5mlg3IwLDIBEREdEompyrQTz1DTIMEhEREY0irUqOggx1rJsRxjBIRERENMrGZjIMEhEREaUsIY6WI2EYJCIiIkphDINEREREKYxhkIiIiCiFMQwSERERpTCGQSIiIqIUxjBIRERElMIYBomIiIhSGMMgERERUQqTxboBqUIURQCA1WqNcUuIiIgo2XXlja780R+GwVFis9kAAIWFhTFuCREREaUKm80GvV7f7zGCeDmRkYYtGAzCZDJBq9WOyBI0VqsVhYWFqK+vh06ni/r5aXTx65l8+DVNLvx6Jpdk/HqKogibzYaCggJIJP2PCmTP4CiRSCQYO3bsiF9Hp9MlzTcy8euZjPg1TS78eiaXZPt6DtQj2IUTSIiIiIhSGMMgERERUQpjGEwSSqUS69evh1KpjHVTKAr49Uw+/JomF349k0uqfz05gYSIiIgohbFnkIiIiCiFMQwSERERpTCGQSIiIqIUxjBIRERElMIYBpPEiy++iHHjxkGlUuHKK6/E3/72t1g3iYbo008/xbe+9S0UFBRAEAT86U9/inWTaIg2bdqEq666ClqtFkajETfffDOqq6tj3SwaopdeegmzZs0KFyYuKyvDX/7yl1g3i6Jk06ZNEAQBDz30UKybMuoYBpPAu+++i4ceegj/8i//gsOHD+Oaa67BsmXLUFdXF+um0RA4HA7Mnj0bmzdvjnVTaJh27dqFH/3oR6isrMSOHTvg9/uxdOlSOByOWDeNhmDs2LF45plncODAARw4cABf+9rXcNNNN+H48eOxbhoN0/79+/HKK69g1qxZsW5KTLC0TBJYsGAB5s6di5deeim8berUqbj55puxadOmGLaMhksQBGzduhU333xzrJtCUdDc3Ayj0Yhdu3bhq1/9aqybQ1GQlZWFf/u3f8Pdd98d66bQENntdsydOxcvvvginnrqKcyZMwcvvPBCrJs1qtgzmOC8Xi8OHjyIpUuXRmxfunQpdu/eHaNWEVFvLBYLgFCAoMQWCATwzjvvwOFwoKysLNbNoWH40Y9+hOXLl+PrX/96rJsSM7JYN4CGp6WlBYFAALm5uRHbc3NzYTabY9QqIrqUKIp4+OGH8ZWvfAUzZsyIdXNoiD7//HOUlZXB7XZDo9Fg69atmDZtWqybRUP0zjvv4NChQ9i/f3+smxJTDINJQhCEiNeiKPbYRkSxs3r1ahw9ehSfffZZrJtCw1BaWoqqqip0dHTgj3/8I+666y7s2rWLgTAB1dfX48EHH8RHH30ElUoV6+bEFMNggjMYDJBKpT16AZuamnr0FhJRbDzwwAPYtm0bPv30U4wdOzbWzaFhUCgUmDhxIgBg3rx52L9/P/7zP/8TW7ZsiXHLaLAOHjyIpqYmXHnlleFtgUAAn376KTZv3gyPxwOpVBrDFo4ejhlMcAqFAldeeSV27NgRsX3Hjh1YtGhRjFpFRECoh3716tV4//33sXPnTowbNy7WTaIoE0URHo8n1s2gIbjuuuvw+eefo6qqKvyYN28evvvd76KqqiplgiDAnsGk8PDDD6O8vBzz5s1DWVkZXnnlFdTV1eG+++6LddNoCOx2O86cORN+XVNTg6qqKmRlZaGoqCiGLaPB+tGPfoS3334bf/7zn6HVasM9+Hq9Hmq1Osato8H653/+ZyxbtgyFhYWw2Wx455138Mknn6CioiLWTaMh0Gq1PcbvpqenIzs7O+XG9TIMJoHbb78dra2teOKJJ9DQ0IAZM2bgww8/RHFxcaybRkNw4MABLFmyJPz64YcfBgDcddddeOONN2LUKhqKrnJPixcvjtj+61//Gt///vdHv0E0LI2NjSgvL0dDQwP0ej1mzZqFiooKfOMb34h104iGhXUGiYiIiFIYxwwSERERpTCGQSIiIqIUxjBIRERElMIYBomIiIhSGMMgERERUQpjGCQiIiJKYQyDRERERCmMYZCIKMY++eQTCIKAjo6OWDeFiFIQwyARURITRRHLli2DIAj405/+FOvmEFEcYhgkIkpiL7zwAgRBiHUziCiOMQwSEUWZKIp47rnnMH78eKjVasyePRvvvfdeeP+HH36IyZMnQ61WY8mSJTh37lzE+zds2IA5c+ZEbHvhhRdQUlISse1Xv/oVpk+fDqVSifz8fKxevTpi/5EjR/Dzn/8cv/rVr6L56RFRkpHFugFERMnm8ccfx/vvv4+XXnoJkyZNwqefforvfe97yMnJwfjx4/Htb38b9913H+6//34cOHAAP/7xjwd9jZdeegkPP/wwnnnmGSxbtgwWiwV///vfw/udTif+8R//EZs3b0ZeXl40Pz0iSjIMg0REUeRwOPDzn/8cO3fuRFlZGQBg/Pjx+Oyzz7BlyxaUlJRg/PjxeP755yEIAkpLS/H555/j2WefHdR1nnrqKfz4xz/Ggw8+GN521VVXhZ+vXbsWixYtwk033RSdT4yIkhbDIBFRFJ04cQJutxvf+MY3IrZ7vV5cccUVcLlcWLhwYcQ4vq7QeLmamppgMplw3XXX9bp/27Zt2LlzJw4fPjz4T4CIUg7DIBFRFAWDQQDAf//3f2PMmDER+5RKJR544IEBzyGRSCCKYsQ2n88Xfq5Wq/t9/86dO3H27FlkZGREbP+Hf/gHXHPNNfjkk08GbAMRpQ6GQSKiKJo2bRqUSiXq6upw7bXX9rr/0hIvlZWVEa9zcnJgNpshimK4B7Gqqiq8X6vVoqSkBP/7v/+LJUuW9LjGY489hh/+8IcR22bOnInnn38e3/rWt4b4mRFRsmIYJCKKIq1Wi0ceeQRr165FMBjEV77yFVitVuzevRsajQb33Xcf/uM//gMPP/ww7r33Xhw8eBBvvPFGxDkWL16M5uZmPPfcc/jOd76DiooK/OUvf4FOpwsfs2HDBtx3330wGo1YtmwZbDYb/v73v+OBBx5AXl5er5NGioqKMG7cuJH+JyCiBMPSMkREUfbkk0/ipz/9KTZt2oSpU6fi+uuvx/bt2zFu3DgUFRXhj3/8I7Zv347Zs2fj5Zdfxs9+9rOI90+dOhUvvvgifvnLX2L27NnYt28fHnnkkYhj7rrrLrzwwgt48cUXMX36dNx44404ffr0aH6aRJQkBPHSgSlERERElDLYM0hERESUwhgGiYiIiFIYwyARERFRCmMYJCIiIkphDINEREREKYxhkIiIiCiFMQwSERERpTCGQSIiIqIUxjBIRERElMIYBomIiIhSGMMgERERUQpjGCQiIiJKYf8fndnJgp6ga88AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = bmb.interpret.plot_slopes(\n",
    "    well_model_interact,\n",
    "    well_idata_interact,\n",
    "    wrt=\"arsenic\",\n",
    "    conditional=None,\n",
    "    average_by=\"educ4\"\n",
    ")\n",
    "fig.set_size_inches(7, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpreting coefficients as an elasticity\n",
    "\n",
    "In some fields, such as economics, it is useful to interpret the results of a regression model in terms of an elasticity (a percent change in $x$ is associated with a percent change in $y$) or semi-elasticity (a unit change in $x$ is associated with a percent change in $y$, or vice versa). Typically, this is achieved by fitting a model where either the outcome and or the covariates are log-transformed. However, since the log transformation is performed by the modeler, to compute elasticities for `slopes` and `plot_slopes`, Bambi \"post-processes\" the predictions to compute the elasticities. Below, it is shown the possible elasticity arguments and how they are computed for `slopes` and `plot_slopes`:\n",
    "\n",
    "- `eyex`: a percentage point increase in $x_1$ is associated with an $n$ percentage point increase in $y$\n",
    "\n",
    "$$\\frac{\\partial \\hat{y}}{\\partial x_1} * \\frac{x_1}{\\hat{y}}$$\n",
    "\n",
    "- `eydx`: a unit increase in $x_1$ is associated with an $n$ percentage point increase in $y$\n",
    "\n",
    "$$\\frac{\\partial \\hat{y}}{\\partial x_1} * \\frac{1}{\\hat{y}}$$\n",
    "\n",
    "- `dyex`: a percentage point increase in $x_1$ is associated with an $n$ unit increase in $y$\n",
    "\n",
    "$$\\frac{\\partial \\hat{y}}{\\partial x_1} * x_1$$\n",
    "\n",
    "Below, each code cell shows the same model, and `wrt` and `conditional` argument, but with a different elasticity (`slope`) argument. By default, `dydx` (a derivative with no post-processing) is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>estimate_type</th>\n",
       "      <th>estimate</th>\n",
       "      <th>lower_3.0%</th>\n",
       "      <th>upper_97.0%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>eyex</td>\n",
       "      <td>-0.524636</td>\n",
       "      <td>-0.654336</td>\n",
       "      <td>-0.402812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      term estimate_type  estimate  lower_3.0%  upper_97.0%\n",
       "0  arsenic          eyex -0.524636   -0.654336    -0.402812"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmb.interpret.slopes(\n",
    "    well_model_interact,\n",
    "    well_idata_interact,\n",
    "    wrt=\"arsenic\",\n",
    "    slope=\"eyex\",\n",
    "    conditional=None,\n",
    "    average_by=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>estimate_type</th>\n",
       "      <th>estimate</th>\n",
       "      <th>lower_3.0%</th>\n",
       "      <th>upper_97.0%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>eydx</td>\n",
       "      <td>-0.286488</td>\n",
       "      <td>-0.352557</td>\n",
       "      <td>-0.223956</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      term estimate_type  estimate  lower_3.0%  upper_97.0%\n",
       "0  arsenic          eydx -0.286488   -0.352557    -0.223956"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmb.interpret.slopes(\n",
    "    well_model_interact,\n",
    "    well_idata_interact,\n",
    "    wrt=\"arsenic\",\n",
    "    slope=\"eydx\",\n",
    "    conditional=None,\n",
    "    average_by=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>term</th>\n",
       "      <th>estimate_type</th>\n",
       "      <th>estimate</th>\n",
       "      <th>lower_3.0%</th>\n",
       "      <th>upper_97.0%</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>arsenic</td>\n",
       "      <td>dyex</td>\n",
       "      <td>-0.167628</td>\n",
       "      <td>-0.201262</td>\n",
       "      <td>-0.136</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      term estimate_type  estimate  lower_3.0%  upper_97.0%\n",
       "0  arsenic          dyex -0.167628   -0.201262       -0.136"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bmb.interpret.slopes(\n",
    "    well_model_interact,\n",
    "    well_idata_interact,\n",
    "    wrt=\"arsenic\",\n",
    "    slope=\"dyex\",\n",
    "    conditional=None,\n",
    "    average_by=True\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`slope` is also an argument for `plot_slopes`. Below, we visualize the elasticity with respect to `arsenic` conditional on a range of `dist100` and `educ4` values (notice this is the same plot as in the _conditional slopes_ section)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x600 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = bmb.interpret.plot_slopes(\n",
    "    well_model_interact,\n",
    "    well_idata_interact,\n",
    "    wrt=\"arsenic\",\n",
    "    conditional={\n",
    "        \"dist100\": np.linspace(0, 4, 50),\n",
    "        \"educ4\": np.arange(0, 5, 1)\n",
    "    },\n",
    "    slope=\"eyex\",\n",
    "    subplot_kwargs={\"main\": \"dist100\", \"group\": \"educ4\", \"panel\": \"educ4\"},\n",
    "    fig_kwargs={\"figsize\": (16, 6), \"sharey\": True, \"tight_layout\": True},\n",
    "    legend=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Categorical covariates\n",
    "\n",
    "As mentioned in the _computing slopes_ section, if you pass a variable with a string or categorical data type, the `comparisons` function will be called to compute the expected difference in group means. Here, we fit the same interaction model as above, albeit, by specifying `educ4` as an ordinal data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv(\"https://vincentarelbundock.github.io/Rdatasets/csv/carData/Wells.csv\")\n",
    "data[\"switch\"] = pd.Categorical(data[\"switch\"])\n",
    "data[\"dist100\"] = data[\"distance\"] / 100\n",
    "data[\"educ4\"] = pd.Categorical(data[\"education\"] / 4, ordered=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Modeling the probability that switch==no\n",
      "Initializing NUTS using jitter+adapt_diag...\n",
      "Multiprocess sampling (4 chains in 4 jobs)\n",
      "NUTS: [Intercept, dist100, arsenic, educ4, dist100:educ4, arsenic:educ4]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c2dc9b046ed2418592ada1e509874438",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sampling 4 chains for 1_000 tune and 1_000 draw iterations (4_000 + 4_000 draws total) took 36 seconds.\n"
     ]
    }
   ],
   "source": [
    "well_model_interact = bmb.Model(\n",
    "    \"switch ~ dist100 + arsenic + educ4 + dist100:educ4 + arsenic:educ4\",\n",
    "    data,\n",
    "    family=\"bernoulli\"\n",
    ")\n",
    "\n",
    "well_idata_interact = well_model_interact.fit(\n",
    "    draws=1000,\n",
    "    target_accept=0.95,\n",
    "    random_seed=1234,\n",
    "    chains=4\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = bmb.interpret.plot_slopes(\n",
    "    well_model_interact,\n",
    "    well_idata_interact,\n",
    "    wrt=\"educ4\",\n",
    "    conditional=\"dist100\",\n",
    "    average_by=\"dist100\"\n",
    ")\n",
    "fig.set_size_inches(7, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As the model was fit with `educ4` as a categorical data type, Bambi recognized this, and calls `comparisons` to compute the differences between each level of `educ4`. As `educ4` contains many category levels, a covariate must be passed to `average_by` in order to perform plotting. Below, we can see this plot is equivalent to `plot_comparisons`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = bmb.interpret.plot_comparisons(\n",
    "    well_model_interact,\n",
    "    well_idata_interact,\n",
    "    contrast=\"educ4\",\n",
    "    conditional=\"dist100\",\n",
    "    average_by=\"dist100\"\n",
    ")\n",
    "fig.set_size_inches(7, 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, computing the predictive difference between each `educ4` level may not be desired. Thus, in `plot_slopes`, as in `plot_comparisons`, if `wrt` is a categorical or string data type, it is possible to specify the `wrt` values. For example, if we wanted to compute the expected difference in probability of switching wells for when `educ4` is $4$ versus $1$ conditional on a range of `dist100` and `arsenic` values, we would pass the following dictionary in the code block below. Please refer to the [comparisons](https://bambinos.github.io/bambi/notebooks/plot_comparisons.html) documentation for more details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = bmb.interpret.plot_slopes(\n",
    "    well_model_interact,\n",
    "    well_idata_interact,\n",
    "    wrt={\"educ4\": [1, 4]},\n",
    "    conditional=\"dist100\",\n",
    "    average_by=\"dist100\"\n",
    ")\n",
    "fig.set_size_inches(7, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Last updated: Sun Sep 28 2025\n",
      "\n",
      "Python implementation: CPython\n",
      "Python version       : 3.13.7\n",
      "IPython version      : 9.4.0\n",
      "\n",
      "arviz : 0.22.0\n",
      "pandas: 2.3.2\n",
      "numpy : 2.3.3\n",
      "bambi : 0.14.1.dev58+gb25742785.d20250928\n",
      "\n",
      "Watermark: 2.5.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -n -u -v -iv -w"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dev",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
